Multi-front procurement recommendation based on query context

ABSTRACT

An improved electronic procurement system is disclosed. The electronic procurement system (“system”) implements a procurement recommendation framework. The system is configured to learn from past online browsing or transactional activities in a community and cause a display of multiple types of recommendations to a user, who has submitted a query, based on an inferred query context.

BENEFIT CLAIMS

This application claims the benefit as a continuation under 35 U.S.C. §120 of application Ser. No. 16/523,736, filed Jul. 26, 2019, whichclaims priority under 35 U.S.C. § 119 application 62/703,678, filed Jul.26, 2018, application 62/765,167, filed Aug. 17, 2018, application62/717,500, filed Aug. 10, 2018, and application 62/720,358, filed Aug.21, 2018, the entire contents of which are hereby incorporated byreference as if fully set forth herein.

FIELD OF THE DISCLOSURE

One technical field of the present disclosure is digital data analysis,transmission, and display. Another technical field is procurementrecommendation based on context data.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Conventional electronic procurement systems allow purchaser procurementsystems to interface directly with seller retail systems in order toprovide streamlined interfaces for searching, selecting, and purchasinggoods. However, many conventional procurement systems have limitedfeatures, interfaces, and access options. It would be helpful to have animproved system for electronic procurement.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

The drawings and detailed description that follow are intended to bemerely illustrative and are not intended to limit the scope of theinvention as contemplated by the inventors.

FIG. 1 illustrates an example networked computer system with whichvarious embodiments may be practiced.

FIG. 2 shows an example adaptive navigation system.

FIG. 3 shows an example method for preparing a collective product wisdomdataset.

FIG. 4 shows an example method for providing intelligent browsing withan adaptive navigation engine.

FIG. 5 shows an example method for federated universal search includingcollective product wisdom.

FIG. 6 shows an example adaptive navigation interface.

FIG. 7 shows an example collective wisdom interface.

FIG. 8 shows an example collective wisdom chat interface.

FIG. 9 shows a schematic diagram of a system and process for selfservice agent creation and self-healing.

FIG. 10 shows an example bookmarklet agent.

FIG. 11 shows an example real-time mobile procurement system.

FIG. 12 shows an example mobile real-time punchout supplier search.

FIG. 13 shows an example mobile real-time universal search.

FIG. 14 shows an example mobile interface home screen.

FIG. 15 shows an example mobile real-time universal search that is inprogress.

FIG. 16 shows an example mobile real-time universal search that isstreaming results.

FIG. 17 shows an example mobile real-time universal search in terms offilters.

FIG. 18 shows an example mobile real-time universal search in terms ofcart additions.

FIG. 19 shows an example mobile real-time universal search in terms offavorites.

FIG. 20 shows an example mobile real-time universal search in terms ofproduct details.

FIG. 21 shows an example mobile real-time procurement in terms ofcreating requisition.

FIG. 22 shows an example mobile cognitive advisor experience.

FIG. 23 shows an example mobile adaptive navigation.

FIG. 24 shows an example data flow for mobile requisitions and orders.

FIG. 25 shows an example data flow for mobile compliant purchasing inexternal stores.

FIG. 26 shows another example data flow for mobile compliant purchasingin external stores.

FIG. 27 shows an example data flow for mobile personal expensereporting.

FIG. 28 shows an example cognitive advisor framework.

FIG. 29 shows an example cognitive advisor framework with threecognitive advisors.

FIG. 30 shows an example interface for providing cognitiverecommendations.

FIG. 31 shows an example cognitive advisor framework for enhancingproduct items, with five unique cognitive advisors.

FIG. 32 shows an example configuration of five cognitive advisors forenhancing product item information.

FIG. 33 shows a diagram comparing data availability and user knowledgein search scenarios.

FIG. 34 shows an example set of inputs to a relevance learning engine.

FIG. 35 shows an example application of the relevance learning engine toa universal search result item.

FIG. 36 shows an example process for continuous enrichment of a globalproduct item master database with relevance learning models.

FIG. 37 shows an example process for adaptive relevance filtering.

FIG. 38 shows an example set of data produced with the relevancelearning engine.

FIG. 39 shows an example process for deep relevance scoring withreal-time adaptive filtering.

FIG. 40 illustrates an example process performed by a server of enablingmultiple cognitive advisors in a cognitive advisor framework forprocessing user queries.

FIG. 41 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

Embodiments are described in sections below according to the followingoutline:

-   -   1. GENERAL OVERVIEW    -   2. EXAMPLE COMPUTING ENVIRONMENTS AND COMPUTER COMPONENTS    -   3. FUNCTIONAL DESCRIPTIONS        -   3.1. REAL-TIME ADAPTIVE NAVIGATION FOR E-PROCUREMENT        -   3.2. SELF-SERVICE AGENT CREATION FOR PROCUREMENT WITH            REAL-TIME SELF-HEALING        -   3.3. REAL-TIME MOBILE PROCUREMENT        -   3.4. REAL-TIME COGNITIVE ADVISOR FRAMEWORK FOR PROCUREMENT        -   3.5. REAL-TIME ADAPTIVE RELEVANCE FOR PROCUREMENT    -   4. EXAMPLE PROCESSES    -   5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW    -   6. EXTENSIONS AND ALTERNATIVES

1. General Overview

An improved electronic procurement system is disclosed. The electronicprocurement system (“system”) implements a procurement recommendationframework. The system is configured to learn from past online browsingor transactional activities in a community and cause a display ofmultiple types of recommendations to a user, who has submitted a query,based on an inferred query context.

In some embodiments, the system is programmed to collect data related tosupplier accounts and buyer accounts, including data related tosuppliers, buyers, products, product searches, production approvalchains or transactions, or supplier or product reviews. The system isprogrammed to further store at least some of the collected data in adatabase. The data can be available at various granularities. Forexample, for suppliers or buyers, the data can be related to industries,organizations, departments, or individuals; for products, the data canbe related to industries, categories, makes, or models. The data can becollected directly from supplier accounts or buyer accounts or fromexternal data sources. The data can be collected in response toprocessing user queries, as further discussed below, or during ordinaryuser online activities. The data can be collected from explicit userinput through graphical user interfaces, such as a button that whenpushed indicates a vote for a particular product as being a good matchfor another product or a comment box configured to accept supplierreviews, or from implicit user behavior that indicates an affinity forparties or items, such as putting a particular item in a shopping cartor paying invoices to a particular supplier within a specific period oftime.

In some embodiments, the system is programmed to maintain a collectionof “cognitive advisors” or recommendation models. Each recommendationmodel has certain required input parameters and produces a procurementrecommendation. Each recommendation can also have various optionalparameters to cover possible information can may be contained in thequery context. A recommendation model can be pretrained based onrepresentative data in the database with machine leaning techniquesknown to one of skilled in the art, in which case the recommendationmodel acts as a classifier. Alternatively, a recommendation model candirectly evaluate individual products or suppliers against the data inthe database in response to a user query. One example recommendationmodel is Best Bets, which may be configured to take a departmentidentifier and a feature identifier and generate a list of productidentifiers that identify products having the highest approval rate interms of the feature identified by the feature identifier among users inthe department identified by the department identifier. The Best Betscan work with products covered by the database or other external sources(e.g., punch-out catalogs) that have different values for the feature(e.g., 0 or 1) identified by the feature identifier and that areassociated with different degrees of approval by buyer accounts ofdifferent departments, such as approved by a first department asevidenced from being voted for, selected, or put into a shopping cart,or disapproved by a second department as evidenced from being ignored,hidden, or voted against. The Best Bets can further assign differentweights to different types of approval or disapproval actions andcompute a recommendation score for each product based on the value forthe feature and the degree of approval using the different weights. Thedifferent types of approval or disapproval actions can correspond tosome of the optional or internal parameters. For example, the action ofputting an item in the cart can correspond to an input parameter of therate of putting this item in the cart over the last three month. TheBest Bets or any other recommendation model can have an additionalparameter used to control the activeness of the optional parameters andthus the extent of database review, as further discussed below.

Another example recommendation model is Bundles, which may be configuredto take a department identifier and a product identifier and generate alist of product identifiers that identify products often purchasedtogether with the product identified by the product identifier by usersin the department identified by the department identifier. Yet anotherexample recommendation model is Savings Max, which may be configured totake a region identifier, a product identifier, a desired quantity, anda current time, and generate a list of supplier identifiers thatidentify suppliers that may offer the best prices in the regionidentified by the region identifier for the product identified by theproduct identifier. The system can be configured to further organize therecommendation models, such as by an execution order. For example, whenthe user query does not refer to a specific product, the Bundlesrecommendation model can be executed only after the Best Betsrecommendation model is executed to generate a list of candidateproducts.

In some embodiments, the system is programmed to receive a user query,generally associated with a buyer account and related to a procurementactivity, such as ordering, approval, purchase, inventory, expensereporting, audit, or market analysis. The system is programmed to nextidentify a query context based on the collected data or other externaldata. The query context can include buyer data derived from the identityof the buyer account, or other data derived from the user query. Forexample, the query may refer to a broad product category or a specificproduct make and model. The query context can then include thecorresponding supplier data. The query may also include general productdescriptions, such as “on sale” or “four-star reviews”, and the querycontext may include data retrieved from internal accounts or externalsources in real time. The system is programmed to ultimately include alist of key-value pairs in the query context, where the key correspondto possible input parameters of the recommendation models and the valuesare included in the user query or derived from the database or externalsources.

In some embodiments, the system is programmed to match the query contextwith the input parameters of each of the recommendation models andinvoke the recommendation models with matching input parameters in acertain order. During the matching process, certain key-value pairs canalso be derived. For example, when the Savings Max recommendation modelhas a specific input parameter for a minimum required savings amount,the value for the specific input parameter if not already included inthe query context can be determined from past values provided by otherusers in the same department as the user submitting the query. Eachrecommendation model can have an additional parameter that controls theactiveness of the optional parameters, as noted above. The value of thisadditional parameter can be determined by the complexity of the query orthe preference of the buyer account. The query could be built word byword over an extended period of time, and the extent of database reviewcan be continuously updated as the query is being built. In oneapproach, when the query is relatively short or simple, the value forthe additional parameter may indicate a larger number of types of useractions associated with products or suppliers in the database are to bereviewed, and thus a larger number of key-value pairs are to be added tothe query context and a larger number of the optional parameters are tobe turned on. In this case, the optional parameters may cover actionsimplicitly showing approval or disapproval or all actions having weightsin a recommendation model above a certain threshold. As the querybecomes more specific, the system can be configured to automaticallyskip reviewing certain parts of the database, to focus on actions ofexplicit approval or disapproval, for example.

In some embodiments, the system is programmed to cause a display of theresults outputted by the invoked recommendation models. All the resultscan be made available to the user through an accordion-style display,for example. Such a display includes a section for each recommendationmodel and the one section that is selected is expanded to show a list ofresults produced by the corresponding recommendation model. The list ofresults can be further expanded in a different portion of the same page.The sections can be ordered by the size of the list of results, aparticular score based on how well the query context matches the inputparameters or the list of results, or a particular weight based on pastuser recommendation selection patterns. Such a display allows the usersubmitting the query to have a clear overview and contrast of the typesof recommendation provided by the system and effectively modify ornarrow down the focus of the search. For example, the user may initiallysubmit a user query that specifies a particular product hoping to getthe product at a low price. After reviewing the recommendations of theproducts used for the same purpose but preferred by others in the sameorganization or the products which are most often approved foracquisition within the same department, the user may decide to order oneof those recommended products instead, especially when that product alsohas a low price or is otherwise selected by multiple recommendationmodels.

The system is programmed to further allow the user submitting the queryto cause a display of an explanation for each list of recommendations,which would be based on the list of input parameter values of thecorresponding recommendation model. A review of the one or moreexplanations may prompt the user to override default or automaticallyinferred values of the input parameters or to update other values toobtain more focused or accurate results. The system is programmed toalso record the user's selection and review of different sections andfurther selection or purchase of the items recommended in the sectionsto enhance the database and thus improve the recommendation models orthe output thereof.

2. Example Computing Environments and Computer Components

FIG. 1 illustrates an example networked computer system with whichvarious embodiments may be practiced. FIG. 1 is shown in simplified,schematic format for purposes of illustrating a clear example and otherembodiments may include more, fewer, or different elements.

In some embodiments, the networked computer system comprises ane-procurement server computer 102 (“server”), one or more suppliercomputers 122 a-n, one or more buyer computers 112 a-m, and one or moredata source computers 132, which are communicatively coupled directly orindirectly via one or more networks 118.

In some embodiments, the server 102 broadly represents one or morecomputers, such as a server farm, a cloud computing platform, or aparallel computer, virtual computing instances, and/or instances of aserver-based application. The server 102 comprises a spend managementlayer 142 that is programmed or configured to host or execute functionsincluding but not limited to managing buyer accounts associated with theone or more buyer computers 112 a-m and supplier accounts associatedwith the one or more supplier computers 122 a-n, and facilitatinggeneration and maintenance of digital documents during procurementtransactions between buyer accounts and supplier accounts, such ascatalogs, purchase requisitions, purchase orders, or invoices. Theserver 102 also comprises a real-time intelligence layer 150 that isprogrammed or configured to host or execute functions including but notlimited to providing real-time intelligence for electronic procurement.

FIG. 1 also illustrates example components of the server 102 inaccordance with the disclosed embodiments. Each of the functionalcomponents can be implemented as software components, general orspecific-purpose hardware components, firmware components, or anycombination thereof. A storage component can be implemented using any ofrelational databases, object databases, flat file systems, or JSONstores. A storage component can be connected to the functionalcomponents locally or through the networks using programmatic calls,remote procedure call (RPC) facilities or a messaging bus. A componentmay or may not be self-contained. Depending upon implementation-specificor other considerations, the components may be centralized ordistributed functionally or physically.

In some embodiments, the fraud detection layer 150 can comprisecomputer-executable instructions, including adaptive navigationinstructions 152, self-service agent creation instructions 154, mobileprocurement instructions 156, cognitive advisor framework instructions158, and adaptive relevance instructions 160. In addition, the server102 can comprise a database module 140.

In some embodiments, the adaptive navigation instructions 152 enableintelligent online navigation of product information based on datarelated to past online activities regarding the products, such asselections, requisitions, or purchases.

In some embodiments, the self-service agent creation instructions 154enable efficient data extraction from various websites based on auser-friendly extraction tool.

In some embodiments, the mobile procurement instructions 156 enableefficient procurement activities via mobile phones which use integratedinput devices to obtain procurement data for further processing by aprocurement server.

In some embodiments, the cognitive advisor framework instructions 158enable intelligent preparation and processing of product queries toenhance procurement experience for specific groups of users orprocurement targets.

In some embodiments, the adaptive relevance instructions 160 enablefiltering or ranking search results based on a combination of relevancescoring algorithms and context-sensitive user input.

In some embodiments, the database module 140 is programmed or configuredto manage relevant data structures and store relevant data for functionsperformed by the server 102. In association with the real-timeintelligence layer 150, the data may include web-browsing histories,input data to web agents, mobile phone data, procurement contexts, orrelevance scores.

In some embodiments, each of the buyer computers 112 a-m broadlyrepresents one or more computers, virtual computing instances, and/orinstances of an e-procurement application program that are associatedwith an institution or entity that is related as a buyer with respect toa separate entity associated with one of the supplier computers 122 a-n.A buyer computer 112 a is programmed to create a buyer account with theserver 102 and manage digital documents related to a buyer accountduring procurement transactions, such as receiving a catalog of itemsfor sale from the server 102, generating or transmitting a purchaserequisition or purchase order for some of the items for sale to theserver 102, or receiving an invoice for some of the items for sale fromthe server 102. The buyer computer 112 a may comprise a desktopcomputer, laptop computer, tablet computer, smartphone, wearable device,or any other type of computing device that is capable of propercommunication with the server 102 as well as adequate local dataprocessing and storage. In some cases, a buyer computer 112 a may be apersonal computer or workstation that hosts or executes a browser andcommunicates via HTTP and HTML over the network 118 with a server-sidee-procurement application hosted or executed at the server 102. In othercases, a buyer computer 112 a may be a server-class computer and/orvirtual computing instance that hosts or executes an instance of ane-procurement application that communicates programmatically via APIcalls, RPC or other programmatic messaging with the server 102.

Similarly, in some embodiments, each of the supplier computer 122 a-nbroadly represents one or more computers, virtual computing instances,and/or instances of an e-procurement application program that areassociated with an institution or entity that is related as a supplierwith respect to a separate entity associated with one of the buyercomputer 112 a-m. A supplier computer 122 a is programmed to create asupplier account with the server 102 and manage digital documentsrelated to a supplier account during procurement transactions, such asgenerating or transmitting a catalog of items for sale to the server102, receiving a purchase order for some of the items for sale from theserver 102, or generating or transmitting an invoice for some of theitems for sale to the server 102. A supplier computer 122 a may comprisea desktop computer, laptop computer, tablet computer, smartphone,wearable device, or any other type of computing device that is capableof proper communication with the server as well as adequate local dataprocessing and storage. In some cases, a supplier computer 122 a may bea personal computer or workstation that hosts or executes a browser andcommunicates via HTTP and HTML over network 118 with a server-sidee-procurement application hosted or executed at the server 102. In othercases, a supplier computer 122 a may be a server-class computer and/orvirtual computing instance that hosts or executes an instance of ane-procurement application that communicates programmatically via APIcalls, RPC or other programmatic messaging with the server 102.

In some embodiments, each of the data source computer 132 a-q broadlyrepresents one or more computers, virtual computing instances, and/orinstances of a data management application program with a communicationinterface. A data source computer 132 a is programmed to manage one ormore data sources, receive a request for certain data in the one or moredata sources from the server 102, and send a response to the request tothe server 102. The data source computer 132 a can comprise anycomputing facility with sufficient computing power in data processing,data storage, and network communication for the above-describedfunctions.

In some embodiments, the network 118 may be implemented by any medium ormechanism that provides for the exchange of data between the variouselements of FIG. 1. Examples of the network 118 include, withoutlimitation, one or more of a cellular network, communicatively coupledwith a data connection to the computing devices over a cellular antenna,a near-field communication (NFC) network, a Local Area Network (LAN), aWide Area Network (WAN), the Internet, a terrestrial or satellite link,etc.

3. Functional Descriptions

The inventors have conceived of novel technology that, for the purposeof illustration, is disclosed herein as applied in the context ofelectronic procurement systems. It should be understood that theinventors' technology is not limited to being implemented in the precisemanners set forth herein, but could be implemented in other mannerswithout undue experimentation by those of ordinary skill in the art inlight of this disclosure. Accordingly, the examples set forth hereinshould be understood as being illustrative only, and should not betreated as limiting.

3.1. Real-Time Adaptive Navigation for E-Procurement

One advantageous improvement for an electronic procurement system is anintelligent, adaptive, real-time “Browse” experience in a federateduniversal search environment for e-procurement Marketplaces, using theCollective Wisdom of the Organization. Federated universal search is aninformation retrieval technology that allows the simultaneous search ofmultiple searchable resources. A user makes a single query request whichis distributed to a plurality of search engines, databases or otherquery engines participating in the federation. The technology underlyingthose search engines, databases, or other query engines, however, may beconstructed in a variety of formats, programming languages, hierarchies,organizational structures, and more. Even the amount of data providedfor specific items may vary greatly between different participants inthe federation. This creates a problem unique in the federatede-procurement space which does not exist in traditional e-procurement.Traditional e-procurement does not procure data in real-time nor is thedata contextualized or tailored to a specific entity's history.Additionally, even in a federated space consisting of specificallycontracted suppliers, those suppliers may still try to upsell orcross-sell additional items that are not relevant to the end-user'sspecific query. Thus, improvements disclosed and variations thereof,provide real-time, dynamic, adaptive navigation of a more relevantresult set to a specific query, in a federated search environment.Additional improvements disclosed herein further enhance the userexperience.

Two common capabilities found in business-to-consumer (B2C) orbusiness-to-business (B2B) eCommerce sites are Search and Browse.However, in the context of an e-procurement system which uses aFederated Universal Search environment that combines hosted catalogswith punchouts to create a real-time universal search experience, aBrowse interface may be challenging to implement since the organizationhosting or maintaining the platform may not be aware of the contents ofthe punchout sites a priori.

That said, a Browse experience in a federated real-time marketplace fore-procurement may be a desirable feature for a variety of reasons. Forexample, Universal Search or even supplier-specific search may not bethe right access paradigm for all procurement needs, such as in thepurchasing of chemicals, biotech cellular samples, and other complexproducts. Due to the syntax and naming structure involved in suchchemical and biological substances, they can be difficult to spell andmay require advanced keyboard functions beyond standard keystrokes toaccurately type. As a result, such items may be complicated to findusing keywords. Since keyword search is the most common queryingapproach for universal search when users are trying to discover productsthat might fit their need, followed perhaps by part-number searches whenuser desires to purchase a specific well-known part or product for whichthe manufacturer part number or supplier part number is already known,difficulty in accurately typing the name of a product can negativelyimpact search results.

As another example, products such as nuts, bolts, washers, and otherhardware have many dimensions and attributes that describe them, whichmay make it difficult to find the correct products using keyword queriesinside universal search. As yet another example, products such asprinter ink, desktop computers, laptop computers, and others whichrequire detailed configuration may have special configuration tools onthe given supplier websites and the need is to get the user directly tothe page on that supplier website for that tool, something that searchis not well suited for, and for which browsing capability may beadvantageous.

Many traditional B2B eCommerce sites, and even traditional e-procurementsolutions support a familiar browsing interface that uses a taxonomy ofcategories that the user can use to click and discover subcategories orproducts available at any level of the taxonomy. This is possiblebecause traditional e-procurement solutions and B2B eCommerce sites bothhave complete knowledge of all products statically stored and availablein advance. In traditional e-procurement systems, these products may befrom multiple suppliers, but are provided in advance to thee-procurement system via electronic catalogs of products by suppliers.In conventional B2B eCommerce sites, these products are already known inadvance from what the eCommerce provider wants to offer to customers.

These traditional approaches taken to providing the user with a browsinginterface are typically not sufficient in a federated real-timee-procurement system for a variety of reasons. For example, in afederated real-time e-procurement system, may not be possible to knowabout all the products from all the suppliers in advance with totalaccuracy up to the moment and in real-time. This is because many of thesupplier connections may be made in real-time to the supplier'spunchout-enabled B2B eCommerce website, and any such website may have adynamically changing product base and availability.

As another example, each supplier integrated into a federated real-timee-procurement system may be using a different product taxonomy. So, forexample, even if the user is searching a set of suppliers, all of whomare “Office Supplies” suppliers, the taxonomy of subcategories in whichthe products are listed for “Office Supplies” can dramatically varyacross the suppliers. As yet another example, the category informationmay be missing or invisible for any given item found from any givensupplier.

As a result, an intelligent browsing experience for the user in afederated real-time e-procurement system could advantageously beconstructed differently than what is found in traditional e-procurementsystems. Disclosed herein is an implementation that provides anintelligence browsing experience based on an Adaptive Navigation enginethat is suitable for a federated universal search environment ine-procurement.

In some embodiments, organization-specific collective product wisdomdataset may be further funneled (with suitable anonymization of data asneeded) to a global Product Item Master 210 database. This may alsoinclude all the chat collaboration and question box collaboration, toenhance the Product Item Master with both behavioral item data (e.g.,selections, purchases), and explicit collective wisdom (e.g., textanalysis of chats and question box).

When such a global Product Item Master database is linked to thereal-time marketplace, the Adaptive Navigation user experience alsoenables tapping into the global (beyond the user's organization)collective wisdom on product item selections and purchases, to enablefaster and more reliable decisions on product selection.

FIG. 2 shows an innovative approach to build an Adaptive Navigation(Intelligent Browse) interface that is suitable for a federateduniversal search environment in e-procurement. Instead of relying on aconventional Browse/Navigation experience for the entire universe ofdata out there, some embodiments may include an intelligent and adaptiveBrowse/Navigation experience that is entirely tuned to the buyerorganization which will be using the tailored federated universal searchenvironment in their e-procurement Marketplace.

Such an Adaptive Navigation feature may tap into the collective wisdom202 of the organization, in terms of the slice of products from theentire universe of available products, that the organization's usershave selected, shown a preference for, or purchased before in one ormore categories. That is, in some embodiments, an Adaptive Navigationfeature may tap into information about item selections 204, itempreferences 206, and item purchases 208 by the aggregate of users withinthe buyer organization, versus all the product data in the entireuniverse of available products in their real-time marketplace.

In some embodiments, an Adaptive Navigation feature may enable a user tobrowse through a taxonomy of products that have been selected orpurchased before, and may include features such as: the taxonomy ofproduct categories used may be configured by the buyer organization inthe e-procurement system, the user can find any product, in any productcategory that has been selected or purchased before, by browsing throughthe taxonomy, the user can browse through the entire organization'sdata, or filter that by their region/division/department/personal asneeded, the categories themselves, and the subcategories within thosecategories, and the items within any given category/subcategory are alllisted in order of selection/preference over “all time” OR in “recenttime” (which could be in the last month or quarter), and the defaultsort-order of items listed within a given category or subcategory isbased on the frequency of selection or purchase of the items within it.

In order to implement an example Adaptive Navigation feature, in someembodiments a new dataset may be created through the intelligent mergingof search additions to the cart, punchout additions to cart, and orderdata that may have happened outside of the marketplace. Such a datasetmay represent the collective wisdom of the organization as it pertainsto product knowledge as it is reflected in product item selections andin product purchases. FIG. 3 shows an example system and method forpreparing a collective product wisdom dataset.

This collective wisdom dataset may be built based upon a variety of useractions 306. For example, a user may select a product in a variety ofways, for example, through searching and the search results page,reviewing an item detail page, reviewing their favorites, reviewingorganization-created bundles or order guides, visiting a supplierpunchout website and finding and adding the item directly to the cart,reviewing a past order, knowing and selecting a product from an externalsystem, or some other method. Such information is collected referred toas the “Selection Context” 302.

Regardless of the Selection Context, in some embodiments a user mayperform one or more actions with a given product selected, for example,a user may consider the product by viewing its details, add the productto the cart, submit the cart with the given product in it as part of arequisition to get approval for purchase, purchase an approved product,or other actions (“Actions” overall).

For any given product that was interacted with in any Selection Context,and upon which any of the Actions were performed, a rich Product Context304 may be captured including information such as: an originatingsupplier and the supplier part number of the item, the manufacturer andthe manufacturer part number of the item, the product category of theitem in the product taxonomy, an item quantity, an item price, aclickable link to the target item page on a given supplier site, or tothe item detail page of a hosted catalog item on a procurement site orplatform, and a user-context including anything that ties the productback to a user, their organization context, or other information.

In addition to this individual product selection information across allproducts selected by all users in the organization, Preferred Products,the Product taxonomy and other such relevant configuration settings mayalso be intelligently merged to create the Collective Product Wisdomdataset. Other potential data points that can be integrated include, forexample, Product ratings in the form of likes/dislikes and/or productstar ratings, Product recommendations in the form of explicit Best Bets,Savings gains from any given product through previous purchases (e.g.,potentially via an analytical dataset that is derived for spend andsavings analytics).

In some embodiments, a collective product wisdom dataset may be updatedcontinuously in real-time to reflect the up-to-the-momentselection/purchase activity. The Adaptive Navigation user experience maybe built on top of this dynamically changing dataset about thecontinuously evolving nature of product knowledge and productpreferences in the organization. In some embodiments, this may be usedto provide a user experience of browsing through a marketplace that isvery dynamic and adapts in real-time. Compared to the conventionalapproach of using all marketplace product data to build a static, andgeneric product browsing experience, an Adaptive Navigation userexperience may be naturally tailored to the company's own productpreference and product purchasing behaviors.

While it may be useful to build an Adaptive Navigation experience fromthe entire organization's collective wisdom on product knowledge andproduct preferences and product purchases, in some embodiments, such asystem may also enable the user to narrow down the browsing to justtheir region (say, they work in US, but organization is global), theirdivision/department (say, they work in the Labs, versus say, the ITdepartment or the Sales department), and even down to their ownindividual selections and purchases to date. In an embodiment thatsupports this functionality, the same browsing experience may elegantlypresent in real-time the slices of collective wisdom that couldinfluence their product choices. For instance, the user may be workingin the Labs in the US within the global organization and wish topurchase a new laptop suitable for running their compute-intensiveanalytical applications. Finding, through browsing (using AdaptiveNavigation), the collective wisdom of their own department in theirregion would provide the most confident pointer to their own productselection in this regard.

FIG. 4 shows an example system and method for providing intelligentbrowsing 402 with an adaptive navigation engine. The “Default” optionsfor View By 404, Time Period 406, and Rank By 408, for intelligentbrowsing may be computed based on a variety of factors, for example: theuser's current needs as found by current search and browsing patterns,which provides signals of the user's need/motivation at this point intime; the user's selections and purchases in the given category area,which may indicate familiarity or non-familiarity with products in thisarea; the user's context, including their department/division/workingregion, and other information; the volume of signals of productselections or purchases in the current category area the user isbrowsing in; the recency of signals of product selections or purchasesin the current category area the user is browsing in; the purchasefrequency of products in the given category; the signals of directrecommendations (“Best Buys”) made by other users for products in thegiven category; the configuration settings around preference forsuppliers and products in the given category; the historical informationof prices for products in the given category; the calculated informationof savings for products purchased in the past in the given category; andother similar information that will be apparent to one of ordinary skillin the art in light of this disclosure.

In some embodiments, a series of weights may then be assigned to each ofthe above criteria and combined with importance of each criteria tocalculate internal scores that dynamically drive the “Default” behaviorsfor the View By, Time Period, and Rank By settings that govern anintelligent browsing experience. In some implementations, the weightsand importance of these criteria may also be provided as advancedsettings that an organization can control about their AdaptiveNavigation experience.

A federated universal search experience in the marketplace may also beenhanced by enabling this collective product wisdom dataset to betransformed into becoming a searchable source of product wisdom. FIG. 5shows an example system and method for a federated universal searchincluding collective product wisdom.

As an example, a real-time updating search index can be created with theCollective Product Wisdom 502 database. This search index of CollectiveProduct Wisdom can be incorporated along with other federated searchsources in real-time, to enhance the user's experience of UniversalSearch. Effectively, the user can benefit not only from supplier datathat is integrated into the system in real-time, but also the CollectiveProduct Wisdom of how the organization prefers, selects and purchasesproducts up to the moment.

FIG. 6 shows an example Adaptive Navigation Interface. The AdaptiveNavigation Interface may have one or more of the following uniqueenhancements not found in conventional navigation interfaces withinmarketplaces: each item listed in the browsing interface reveals clearlythe number of times the item has been selected 602/purchased 604 and itstrend over time, each item listed in the browsing interface also revealsthe insight around the trend of selection/purchase acrossregions/divisions/departments, each item listed in the browsinginterface also reveals the most recent individual selectors/purchasersof the given item.

FIG. 7 shows collective wisdom associated with product viewable on theAdaptive Navigation Interface, including cart selections 702, purchases704, and people involved. As shown in FIG. 7, Adaptive Navigation mayalso facilitate internal organization collaboration to tap into thecollective wisdom of the past individual selectors/purchasers of thegiven item through one or more of the following integrated tools: areal-time chat interface with a selected user (past selector/purchaserof the product), a real-time chat interface with more than one selecteduser or the entire group of selected users (who have selected orpurchased this product before), all real-time chats are saved andpreserved within the item detail page for the given item, and aresearchable sources of information on product details, a question boxthat automatically enables collaboration between buying user andprevious selected users, the content within which is available to allusers, and a searchable source of information on more collective wisdomwithin the organization about the product

FIG. 8 shows an example collective wisdom interface with chat features802. This chat experience may be advantageous for a variety of reasons,for example: the chat may be purposeful, not casual, and specificallyaround helping with purchasing a product for a given need in ane-procurement system; the chat may be centered around the specificproduct in question, that the browsing user is considering; the chatmembers may be dynamically assembled from the “Past Users” 806, (i.e.users within the organization who have experience in selecting theproduct for consideration or purchasing the product in the past); thepotential chat members 808 may be automatically sorted to indicate thosewith the most recent selection/purchasing history with the specificproduct in question, the most experience with product purchases in thiscategory, the most experience with working in the organization withproduct purchase experience in this category and such, or othercriteria; the chat interface 804 itself may operate synchronously (e.g.,pulling selected chat members into a real-time chat experience), orasynchronously (e.g., informing selected chat members of the chathistory of the chat while they weren't online), and archives itself as arelevant dataset that provides signals of the organization's knowledgearound the topic of the specific product in question, a dataset that,when mined, can bring additional signals of wisdom to include in theCollective Product Wisdom database

Additionally, in some embodiments, a Question Box 810 may present aspecialized interface for users within the organization to ask questionsand get answers and view and review answers and rate the answers arounda specific product. In some embodiments, Question Box data may begathered and structured to enable the system to mine and distilladditional signals of knowledge and preference within the organization,around the given product, and continually update the Collective ProductWisdom dataset to reflect this.

Adaptive Navigation is a building block that enables new capabilitiesfor e-procurement. For example, a new real-time merchandizing frameworkthat will let supplier partners place special offers in any givencategory level; provide entire sponsored products segment; and otheradvantages. These may be particularly useful capabilities when ane-procurement system is used in the context of powering specializedmarketplaces. As another example, the proposed Cognitive Advisorframework could be integrated into the Browse interface, and with thatin place, a “Related Products” cognitive advisor could recommend relatedproducts to browse from any given page, based on an analysis of productsfrequently selected/purchased together (or some other suitablealgorithm).

It should be understood that any one or more of the teachings,expressions, embodiments, examples, etc. described herein may becombined with any one or more of the other teachings, expressions,embodiments, examples, etc. that are described herein. Thefollowing-described teachings, expressions, embodiments, examples, etc.should therefore not be viewed in isolation relative to each other.Various suitable ways in which the teachings herein may be combined willbe readily apparent to those of ordinary skill in the art in view of theteachings herein. Such modifications and variations are intended to beincluded within the scope of the claims.

Having shown and described various embodiments of the present invention,further adaptations of the methods and systems described herein may beaccomplished by appropriate modifications by one of ordinary skill inthe art without departing from the scope of the present invention.Several of such potential modifications have been mentioned, and otherswill be apparent to those skilled in the art. For instance, theexamples, embodiments, geometrics, materials, dimensions, ratios, steps,and the like discussed above are illustrative and are not required.Accordingly, the scope of the present invention should be considered interms of the following claims and is understood not to be limited to thedetails of structure and operation shown and described in thespecification and drawings.

3.2. Self-Service Agent Creation for Procurement with Real-TimeSelf-Healing

Unlike many traditional e-procurement systems, real-time procurementsystems utilizing federated search may require instant access tosupplier websites. With dynamic and constantly changing content reliedupon by real-time procurement systems, it may be advantageous to applycertain innovations disclosed herein which interact with a variety ofwebsites in a variety of formats in near instantaneous ways so that datacan be gathered and presented to the user.

Federated search systems bring numerous advantages by allowing a user tosearch across live data from a variety of data sources. However, toimplement federated search, each data source may require that itimplement a standardized interface, or an agent may be required becreated to translate the custom data source to a standardized interfacewhich the federator can accept. Many current systems for creating theseagents can be time consuming. Furthermore, it can require expert staffto build agents. In turn, it is advantageous to have a simple system forcreation of agents that non-expert users can utilize.

Another challenge with agents is in maintaining them to be in sync withchanges in structure and format and content at the target data sourcesthat they connect to. Without such routine maintenance, agents can fallout of sync with the target data sources and degrade the federatedsearch experience for the users, which in turn may result in a poor userexperience, lower user satisfaction and declining user confidence in thesystem. Just like with agent creation, it can require expert staff tomaintain agents as well. Even with expert staff, the maintenanceactivity may delay the point in time when an agent is detected to havefallen out of synch with its target data source. And in this timewindow, there may be a degradation in the federated search experience.

Using aspects of the technology disclosed herein, a system such as thatshown in FIG. 9 could be implemented that would allow non-expert usersto create agents that are simple to create, robust, and incorporatereal-time self-healing. Such a system could be both for owners andoperators or data sources and for third party service providers toprovide agents for those data sources.

In some example federated systems, the first step may be sourceselection. This may be used to determine the type of data available andthe technology to be applied to gather it. In modern ecommerce, thisdata exists in live websites in custom formats. These might includelarge vendors selling broad commodity goods such as office suppliers,maintenance repair & operations (MRO), and IT hardware and accessories.Active suppliers may change inventory and prices rapidly and aretypically selected as federated search sources.

One aspect that may be included in this type of system is self-serviceagent creation. Most data sources of interest have a website, so aprimary use case for agents is to connect a federator to a website. Inturn, the initial selection of technology could be browser focused. Thisimplementation could include a web-based workflow engine and abookmarklet 902, as further illustrated in FIG. 10. The workflow enginewould list the page types it needs to understand and whether they havebeen completed. It would also provide a bookmarklet ready for draggingto the browser.

Agent creation may be required to connect a website to the federator. Agoal of agent creation may be to create custom configuration data andlogic to navigate and retrieve data from a selected data source. This isotherwise known as “user modeling” to capture user behavior and the datapresented to the user. This user modeling allows the agent to navigateand retrieve data from the web site.

One novel aspect of the disclosed technology is the creation process andthe use of the information gathered in that process for agent creationand maintenance. Unlike traditional methods that either rely on purevisual selection or HTML based selection, some embodiments of thedisclosed technology may interact with the user at the text level andautomatically build the appropriate configuration and logic fornavigation and extraction. This may provide improvements overconventional methods in the way an embodiment interacts with the userand may also reflect innovations in the way certain embodiments maygather, store, process, and/or update that configuration data and logic.

Some embodiments of the disclosed technology may provide a bookmarkletthat a user interacts with. A bookmarklet is a small installable modulefor a browser that can enhance the functionality available in a browser.It is cross browser compatible so can be used on a wide variety of Webbrowsers. In some embodiments of the disclosed technology, a bookmarkletmay be used to interact with a user at the text level, and may bespecialized and loaded with code for this purpose. One primary use casefor this type of system would be for procurement software which wouldfocus on various page types, including Search pages, Detail pages, andCheckout pages.

Initially, in some embodiments where bookmarklets are present abookmarklet may be used to make a page selection from a target website.The user could then go to the target website and go to a page theywanted to capture and launch the bookmarklet. The bookmarklet wouldbring up a form where the user could select the page type 1002 (e.g.,Search, Detail, Checkout). Once a page type was selected, it would showthe fields 1004 expected for that page type, which would be familiar tothe user. For example, an eCommerce website typically has an item titleand a price. However, the user would not need to understand websitetechnology like HTML, and could instead recognize item title and price.

At a next step, a user may identify the text of interest for a field ina user text selection process 904. For example, the user might choose togather the manufacturer of the item. This could be performed by, forexample, using a visual browser to identify the HTML element of interestor enter some specification based on the HTML spec (e.g., HTML XPATH ora CSS class).

The user could identify the text of interest in multiple ways. In someembodiments, the user could use the mouse to drag text to thebookmarklet. In some embodiments, the user could enter the text ofinterest manually.

For items that appear multiple times, the user would identify the firstand last item using these methods. In some embodiments, once the texthas been identified with the bookmarklet, the page could be searched forthat text to identify all elements matching that text as well as the rawstructural data such as the HTML XPATH or CSS classes. In someembodiments of this type, as the user marks text, this information maybe saved to a database in a user text storage process 906 to create adataset of text snippets and HTML signatures tied to that URL fortesting and self-healing.

When the user identifies an element, it copies the text, but it alsodetermines a signature in a signature generation process 912, thesignature being a string that represents that selection on the pagealong with all the HTML details about position in a position analysisprocess 908. The code that builds the selector 910 takes two signaturesand generates an optimized rule that captures the data but is largelyimmune to unrelated page changes to keep it from being as brittle. Astored signature could also be used to validate the page. It would alsoallow this marking to be done on multiple pages (i.e., some sites havevarious ways they show search results) and then “compile” that info intoa set of rules, which could work across different page types acrosscomplex web sites.

In some embodiments, the underlying code builds a normalized stringrepresentation of the path. This not only includes a CSS selector, butposition information within the entire document (e.g., the 5th H3element) and within the localized area (e.g., the 3rd sibling H3). Thisgives richer position information for pattern matching. Pattern matchingalgorithms can then be applied to the normalized string representationto allow a minimal rule to be built which means all the positionalrequirements with the minimal amount of specificity. This makes the rulerobust and immune to unrelated change on the page, something thattraditional pure HTML path-based methods do not accomplish. Table 1shows an example set of software code that could be executed toimplement positional information for the signature as described above.With the signature information available, table 2 shows an example setof software code that could be executed to build a selectorautomatically using the global and sibling path parts inside thesignature.

TABLE 1 Example software code for building positional information for asignature function addPositions(pathpartdetail,node,selector) {  varallnodes = aqjQuery(selector); pathpartdetail.globalPos=allnodes.index(node)+1; pathpartdetail.globalCount=allnodes.length;  var siblingnodes =aqjQuery(node).parent( ).children(selector); pathpartdetail.siblingPos=siblingnodes.index(node)+1; pathpartdetail.siblingCount=siblingnodes.length; } functiongetSelectedPathParts(selection) {  var pathparts=[];  var targetNode; if (selection.anchorNode==selection.focusNode)  targetNode=aqjQuery(selection.anchorNode).parents( );  else  targetNode=aqjQuery(selection.anchorNode).parents( ).has(selection.focusNode);  targetNode.each(function( ) {   var pathpart= new Object( );   pathpart.node=new Object( );  pathpart.node.name=this.nodeName;  addPositions(pathpart.node,this,this.nodeName);   if (this.id!=″) {   pathpartid=new Object( );    pathpart.id.name=this.id;   addPositions(pathpart.id,this,″#″+this.id);   }   if(this.className!=″) {    pathpart.classes=[];    var classes =this.className.split(″ ″);    for (var i = 0; i < classes.length; i++) {    if (classes[i]!=″) {      var classpart=new Object( );     classpart.name=classes[i];     addPositions(classpart,this,″.″+classes[i]);     pathpart.classes.push(classpart);     }    }   }  pathparts.unshift(pathpart);  });  return pathparts; }

TABLE 2 Example software code for building a selector from a signaturefunction findKeySelector(firstpathparts,lastpathparts) {  varselector=″!NONE″;  var firstpos=firstpathparts.length-1;  varlastpos=lastpathparts.length-1;  while(firstpos>=0 && lastpos>=0) {  var firstpathpart=firstpathparts[firstpos];   varlastpathpart=lastpathparts[lastpos];   if(firstpathpart.node.name!=lastpathpart.node.name)    return ″!MISMATCH″;  if (firstpathpart.node.globalPos==1 &&lastpathpart.node.globalPos==lastpathpart.node.globalCount) {   selector=firstpathpart.node.name;    break;   }  if(firstpathpart.hasOwnProperty(″id″) &&lastpathpart.hasOwnProperty(″id″)) {   if(firstpathpart.id.name==lastpathpart.id.name) {    if(firstpathpart.id.globalPos==1 &&lastpathpart.id.globalPos==lastpathpart.id.globalCount) {    selector=″#″+firstpathpartid.name;     break;    }   }  }  if(firstpathpart.hasOwnProperty(″classes″) &&lastpathpart.hasOwnProperty(″classes″)) {   var match=false;   for (vari=0; i<firstpathpart.classeslength; i++) {    for (var j=0;j<lastpathpart.classes.length; j++) {     if(firstpathpart.classes[i].name==lastpathpart.classes[j].name) {      if(firstpathpart.classes[i].globalPos==1 &&lastpathpart.classes[j].globalPos==lastpathpart.classes[j].globalCount){       selector=″.″+firstpathpart.classes[i].name;       match=true;      break;      }     }    }   }   if (match)    break;  } firstpos--;  lastpos--;  }  if (selector!=″!NONE″) {   firstpos++;  lastpos++;   while(firstpos<firstpathparts.length &&lastpos<lastpathparts.length) {    varfirstpathpart=firstpathparts[firstpos];    varlastpathpart=lastpathparts [lastpos];    if(firstpathpart.node.name!=lastpathpart.node.name)     returnselector+″!MISMATCH″;    var append=″;    if(firstpathpart.node.siblingPos==1 &&lastpathpart.node.siblingPos==lastpathpart.node.siblingCount) {    selector+=″>″+firstpathpart.node.name;    }    else if(firstpathpart.node.siblingPos==lastpathpart.node.siblingPos) {    selector+=″>″+firstpathpart.node.name;     if(firstpathpart.node.siblingCount>1)     selector+=″:nth-of-type(″+firstpathpart.node.siblingPos+″)″;    }   else if (firstpathparthas.OwnProperty(″id″) &&lastpathpart.hasOwnProperty(″id″) && firstpathpart.id.siblingPos==1 &&lastpathpart.id.siblingPos==lastpathpart.id.siblingCount) {    selector+=″>#″+firstpathpart.id.name;    }   else if(firstpathpart.hasOwnProperty(″id″) && lastpathpart.hasOwnProperty(″id″)&& firstpathpart.id.siblingPos==lastpathpart.id.siblingPos) {   selector+=″>#″+firstpathpart.id.name;    if(firstpathpart.id.siblingCount>1)    selector+=″:nth-of-type(″+firstpathpart.id.siblingPos+″)″;   }  else if (firstpathpart.hasOwnProperty(″classes″) &&lastpathpart.hasOwnProperty(″classes″)) {    var match=false;     for(var i=0; i<firstpathpart.classes.length; i++) {      for (var j=0;j<lastpathpart.classes.length; j++) {       if(firstpathpart.classes[i].name==lastpathpart.classes[j].name) {       if (firstpathpart.classes[i].siblingPos==1 &&lastpathpart.classes[i].siblingPos==lastpathpart.classes[i].siblingCount){         selector+=″>.″+firstpathpart.classes[i].name;        match=true;         break;        } else if(firstpathpart.classes[i].siblingPos==lastpathpart.classes[i].siblingPos){         selector+=″>.″+firstpathpart.classes[i].name;         if(firstpathpart.classes[i].siblingCount>1)         selector+=″:nth-of-type(″+firstpathpart.classes[i].siblingPos+″)″;        match=true;         break;        }       }      }     }     if(!match)      return selector+″!CHILDMISMATCH-″+firstpathpart.node.name;   }    else     returnselector+″!CHILDMISMATCH-″+firstpathpart.node.name;    firstpos++;   lastpos++;   }  }  return selector; }

Once the bookmarklet has the structural element (or the first and lastelement for collections of elements), it may automatically determine aCSS selector rule and then immediately test it on the same page showingthe count and the items captured. In this manner, the process issimplified for some embodiments so that the user only has to highlighttext or an item and press a button, then briefly check the results. Theuser may not need to understand CSS selectors (or other relevantelements, depending on the embodiment) of the underlying technology,though in some embodiments such views could be provided in an expertpanel for those expert users that wish to see it.

As an example of the described process, a user might identify the firstand last item on the page. The automated bookmarklet would identify theCSS class of that item. For example, the class might be item. It wouldthen list all items for verification by the user.

The user would then repeat for each page type and the workflow wouldshow the agent was complete. All associated information may be stored ina database for later use. In some embodiments, this may include the URLoff each page, original text, the signature, the automatically createdselector, and the data gathered by the selector. Unlike traditionalsystems, in some embodiments of the disclosed technology, originalpieces of information associated with the text may be saved for lateruse. This may be advantageous for and enable later maintainabilitybecause it captures the user interaction in gathering text separate fromthe algorithm to determine position. This can be used to allow automatedtasks to regenerate the later steps automatically without requiring userinteraction which supports self-healing.

Further improvements would focus on workflow within the data source.There is a fair amount of the complexity around handling all theworkflow in an agent (e.g., clicking to expand sections so that it canview expanded data). In turn, the bookmarklet may advantageously havecapabilities in that area.

One other advantage of this approach is that it is standards basedaround HTML/CSS/JS. They do not require special plugins or developmenttools. Every modern browser has what we need built in. Furthermore, forexpert users, the rules would be understandable and could be viewed andedited using standard test frameworks (e.g. a jsfiddle type interface tocustomize and test the code).

For “standard” data sources (e.g., magenta or shopify), the bookmarkletcould be preloaded with rules and automatically retrieve information forverification. The end user would just need to verify the results tocreate an agent and edit anything they wanted to change.

The disclosed system is also capable of real-time self-healing. This ispossible through use of the data the bookmarklet captures for rebuildinga rule from text enabling the system for self-monitoring. Since anembodiment may know the text that is desired, it can continuouslymonitor the results of the selector in a self-healing monitoring process914 to ensure it continues to gather the proper text. The system can usethe saved text to find that text in a data extraction process 916 andgenerate the signature and then automatically create a CSS rule thatallows for self-healing. It then has the tools to automatically identifythe text, as if the user entered it, and build an updated rule. Bystoring text snippets, the agent creation process can be recreated as ifa user was doing it, but without user action and generate a new updateagent matching the updated data source.

For self-healing, the URL, the expected text, and the previous resultsfrom the selector are retrieved from the database. The URL can then beretrieved to gather the data in the current website structure, whichmight have changed since original creation. The self-healing agentfeature can then regenerate signatures and selectors based on text. Ifit generates the same data as the stored data from the previous results,the agent can be updated with the updated selector and no userinteraction is required.

In some embodiments, if there are deviations in results, the differencescan be presented to a user in a custom UI showing the previous text andnew text. In some implementations this may be accomplished by colorcoding the presented text similarities in difference. This may be usedto support quick validation by the user, and avoid the time and cost ofnew agent creation.

In turn, an agent deployed in a production federated search environmentcan use automated error checking in working with a target web site, andtrigger the self-healing step as needed, and recover from such errorsgracefully in real-time. This approach may dramatically reduce manualmaintenance on the part of experts to maintain agents, and maydramatically increase the uptime of a satisfying federated searchexperience for users.

It should be understood that any one or more of the teachings,expressions, embodiments, examples, etc. described herein may becombined with any one or more of the other teachings, expressions,embodiments, examples, etc. that are described herein. Thefollowing-described teachings, expressions, embodiments, examples, etc.should therefore not be viewed in isolation relative to each other.Various suitable ways in which the teachings herein may be combined willbe readily apparent to those of ordinary skill in the art in view of theteachings herein. Such modifications and variations are intended to beincluded within the scope of the claims.

Having shown and described various embodiments of the present invention,further adaptations of the methods and systems described herein may beaccomplished by appropriate modifications by one of ordinary skill inthe art without departing from the scope of the present invention.Several of such potential modifications have been mentioned, and otherswill be apparent to those skilled in the art. For instance, theexamples, embodiments, geometrics, materials, dimensions, ratios, steps,and the like discussed above are illustrative and are not required.Accordingly, the scope of the present invention should be considered interms of the following claims and is understood not to be limited to thedetails of structure and operation shown and described in thespecification and drawings.

3.3. Real-Time Mobile Procurement

With the rise of smart phones and devices with personal assistants (likeAlexa, Google, Siri etc.), people are increasingly exposed to faster andnovel ways of getting things done. Touch, tap, speak, gesture and getthings done no matter where you are. With the rise in success of AItechnologies, have a personal assistant remind you, do things for you.This is a world far removed from the more complex, confusing world ofe-procurement.

The Case for Real-Time Mobile Procurement

Mobile smart phones and device use is dramatically increasing and isalready becoming ubiquitous in a personal setting. In the workplace,which always seems to lag the personal/consumer experience, security,access, and other rules tend to clamp down on the freedom and ease ofaccess that the mobile-armed person desires today.

In a Procurement context, vendors have made efforts to extendtraditional e-procurement systems to a mobile environment to streamlinesteps of the procure-to-pay (P2P) process, like receiving notifications,reviewing and approving requisitions and the like.

Vendors have also brought a shopping experience to the mobile to enablea professional to make purchases on the go. However, these attempts arelimited to exposing only the product content available via hostedcatalogs.

Furthermore, as new interaction methods are taking root in a person'spersonal experience with mobile and smart devices, there is theopportunity to rethink their application to procurement functions.

Furthermore, there are notable reasons why this sort of mobileexperience of real-time universal search for e-procurement is notfeasible with traditional approaches today. Supplier websites are notuniformly mobile enabled. Not all suppliers have a mobile enabledwebsite, and as a result are nearly unusable by users in a mobileenvironment. Within the subset of suppliers with good mobile enabledwebsites, not all of them support a punchout capability, which is abasic building block for being able to connect them into a e-procurementsystem in a mobile environment. Due to network security issues, a usermay not even be able to get to the enterprise resource planning (ERP)from the mobile device to start with, especially if the user istraveling and are not there on their organization's network. Yet, thevalue of a mobile e-procurement experience for a user is to let themaccess it regardless of where they are located at a given point in time,and search and buy what they need from anywhere. Additionally, theuser's e-procurement experience typically starts by punching out from anERP application to the e-procurement system. ERP mobile access, likesupplier web site mobile access, is not uniformly available across theERP systems in the market today.

A system is disclosed for real-time mobile e-procurement with theability to deliver a real-time universal search experience and otherreal-time capabilities to the user via a mobile app, while fitting intothe security architecture of the organization, the connection neededwith the ERP, and the connection needed with myriad, diverse, supplierpunchout websites.

FIG. 11 illustrates an environment in which embodiments of the systemcan be implemented. In some embodiments, the system 1102 manages anagent library 1104 with respect to a supplier universe 1106 to improvemobile procurement.

Mobile Real-Time Punchout Supplier Search

Punchout technology was originally created to enable an ERP system or aProcurement system to securely access a tailored supplier website whichhas been suitably customized to reflect the contract between thatsupplier and the buying organization. Supplier websites are either notmobile enabled in the first place, and if they are, they may not bemobile punchout enabled. For these reasons, it is difficult to get toand search an individual supplier's punchout website.

FIG. 12 shows an example mobile real-time punchout supplier search. Insome embodiments, the system is configured to let the mobile user beable to search or browse a given supplier's punchout site within themobile experience. The user of the mobile app is authenticated with theERP via a server-side process. An authenticated user is able to accessan interface on the mobile app 1202 to search a given supplier'spunchout website. In the mobile search interface of a given supplier'spunchout website, the user is able to do a query like they would be ableto via the e-procurement system's website. The query is relayed to aserver-side process via an API 1204 that is tuned to perform a search1206 the given supplier's punchout website. The server-side processinitiates the “agent” 1208 configured to search the given supplierpunchout website 1210. The configured agent for the given supplierpunchout website logs into the supplier punchout website and interactswith it in real-time and streams results and status back to the callingserver-side process. The real-time results and status are streamed bythe server-side process back to the mobile device. The mobile deviceimplements a real-time streaming user experience that streams the foundresults from the supplier's punchout website so that the user is able tosee results as they arrive.

In some embodiments, the user is further able to view the item detailpage of a given search result from the supplier's punchout website. Whenthe user taps on the item within the search results in the mobileinterface, the mobile app relays the action via an API that is tuned tofetch item details 1212 for the given item from the given supplier'spunchout website. The server-side process initiates an “agent” requestto the agent that is configured to interact with the given supplierwebsite. The configured agent, in turn, initiates an interaction withthe supplier punchout website to walk into the item detail page,interact with the item detail page to discern the specific item detailson the page, including item description, item attributes and itemspecifications if any, item images and videos if any, and transform thatinto a structured form, and send it back to the calling server-sideprocess. The item details in structured form are streamed by theserver-side process back to the mobile device. The mobile deviceimplements a suitable rendering of item details including anyinteractions possible from the item details page like item comparison,item checkout etc.

Mobile Real-Time Universal Search

The foundation for real-time mobile procurement is the ability to accessthe entirety of available product assets and information assets via areal-time universal search. This would include all product catalogs,contracts, policies, bundles, order guides, supplier punchout sites,inventory and more, all made accessible via a single mobile real-timeuniversal search. This would enable a person to avoid dependence ontheir desktop-based access to the procurement marketplace, and be ableto do their purchasing on the mobile, compliant with procurementstrategies.

FIG. 13 shows an example mobile real-time universal search. In someembodiments, a system is disclosed for enabling to the user to benefitfrom the entire real-time universal search experience from the mobiledevice. The user of the mobile app is authenticated with the ERP via aserver-side process. An authenticated user is able to access aninterface on the mobile app 1302 to search a given supplier's punchoutwebsite. In the mobile search interface for universal search, the useris able to do a query like they would be able to via the e-procurementsystem's website. The query is relayed to a server-side process via anAPI 1304 that is tuned to perform a search 1306 the given supplier'spunchout website. The server-side performs the guided buying processwhich selects the best suppliers suitable for the given query based onmachine learning and supplier configurations. The server-side processinitiates the “agents” 1308 configured to search the selected supplierpunchout websites, and also initiates searches of hosted internetsources 1310 including catalogs, bundles, order guides, contracts,policies, inventory data sources in parallel. In the event of searchinga given supplier punchout website, the “agent” for that supplierpunchout website logs into the supplier punchout website and interactswith it in real-time and streams results and status back to the callingserver-side process. The real-time results from all selected sources inuniversal search, as well as status updates are streamed by theserver-side process back to the mobile device. The mobile deviceimplements a real-time streaming user experience that streams the foundresults from the universal search so that the user is able to seeresults as they arrive. Relevance ranking and organization preference ofsupplier and items as the case might be are applied to get the finalrelevance ranked list for the user on the mobile device.

In some embodiments, the user is further able to view the item detailpage of a given search result from the supplier's punchout website. Whenthe user taps on the item within the search results in the mobileinterface, the mobile app relays the action via an API that is tuned tofetch item details 1312 for the given item from the given supplier'spunchout website. The server-side process initiates an “agent” requestto the agent that is configured to interact with the given supplierwebsite. The configured agent, in turn, initiates an interaction withthe supplier punchout website to walk into the item detail page,interact with the item detail page to discern the specific item detailson the page, including item description, item attributes and itemspecifications if any, item images and videos if any, and transform thatinto a structured form, and send it back to the calling server-sideprocess. The item details in structured form are streamed by theserver-side process back to the mobile device. The mobile deviceimplements a suitable rendering of item details including anyinteractions possible from the item details page like item comparison,item checkout etc.

In some embodiments, when user adds an item to the mobile cart, acompliance check is performed on the item to ensure it matches with thesourced contract for that supplier. A server-side process receives acompliance check request, and dispatches the “agent” configured for thatsupplier punchout website, as applicable, and fetches the price of thespecific chosen item, and checks that against the contract for thatsupplier in the system in a compliance check 1314. The server-sideprocess streams the results of the compliance check back to the mobiledevice.

In some embodiments, real-time information on pricing, availability,shipping options are not always visible via traditional e-procurementsystems. Within an item detail page or in the mobile cart interface, thepricing/availability/shipping and other details can be gathered from thegiven supplier's punchout website in real-time. A server-side processreceives the user's request to get real-timepricing/availability/shipping information 1316 and uses the “agent”configured for the given supplier's punchout website and interacts withthe given item's page(s) on the given supplier punchout website andbrings back the needed information as present. The mobile app presentsthe streamed information from the server-side as needed for the user toconsume this information.

In some embodiments, in a private marketplace that is configured to havecompetitive suppliers in purchasing categories, a real-time check foralternative suppliers can be made, for the items selected by the user inthe mobile cart. From the mobile cart, when the user initiates a mobilecheckout action, the app initiates a request to find if there arealternative suppliers providing the items selected in the cart alongwith considerations for preferred items, lower priced items, items withdifferent delivery windows and lower shipping cost or faster shippingetc. A server-side process kicks of a specialized search for alternativesuppliers 1308 for each one of the selected items in the mobile cart inparallel, and as matches appear, and verified for relevance, the itemsare streamed back to the mobile device. The mobile interface, in turn,presents potential alternative supplier choices with the relevantinformation surrounding the same, and lets the user pick and replace themobile cart item in question with better alternatives that may beprovided.

In some embodiments, often, for a given product selection made by theuser, there may be other comparable brands or models or packagingvariants, that may be better selections from a pricing, availability orother considerations. The mobile interface may present to the user theoption to consider broadly comparable items to the one they areconsidering adding to the mobile cart. In this event, the mobileinterface requests the server for comparable items for the selected itemin question. The server-side initiates a lookup for comparable items1320 using the Global Item Master Database 1322 for items that aresimilar to the given item being selected, and as candidate results arefound, they are streamed back to the mobile app with the relevantdetails to enable the user to make wiser choices on their itemselections. The mobile interface presents the comparable items with thechoice for the user to swap the item currently selected with an itemfrom the comparable items set.

In some embodiments, the system is configured to determine whether thisis a good time to purchase a given product. Based on historical pricinginformation and a price prediction algorithm that models pricing trends,a price trend and price prediction can be made. This can be based onindividual product pricing trends or can be based on market baskets forselected categories. The mobile interface initiates a request forpricing predictions and a server-side process performs a price trend andprediction lookup, which in turn, uses the pricing history data from theGlobal Item Master database 1322 as needed to provide pricing trend andprediction information 1324 back to the user. The pricing trend andprediction information can be suitably presented, potentially with achart on the pricing trend, and potentially with a price prediction andpurchasing recommendation.

FIGS. 14-21 show example screens of a user's mobile phone that interactswith the system in universal search activities. FIG. 14 shows an examplemobile interface home screen. FIG. 15 shows an example mobile real-timeuniversal search that is in progress. FIG. 16 shows an example mobilereal-time universal search that is streaming results. FIG. 17 shows anexample mobile real-time universal search in terms of filters. FIG. 18shows an example mobile real-time procurement in terms of creatingrequisition. FIG. 19 shows an example data flow for mobile personalexpense reporting. FIG. 20 shows an example mobile real-time universalsearch in terms of filters. FIG. 21 shows an example mobile real-timeuniversal search in terms of cart additions.

Mobile Cognitive Advisor Experience

The smaller device footprint and the desire for immediacy in a mobileenvironment dictate the need for tuning the user experience ofpresenting and interacting with Cognitive Advisors, as further discussedin Section 3.4.

FIG. 22 shows an example mobile real-time universal search in terms offavorites. In some embodiments, Cognitive Advisors for Enhancing QueryResults 2202 would be accompanied by a mobile recommendation priorityconfiguration would be applied on top of Cognitive Advisorrecommendations in the context of searching. For instance, Best Bets andBundles 2204 Cognitive Advisor recommendations would supersede otherrecommendations and the user experience will guide the user to thesefirst, as these represent the highest signals of relevance.Recommendations from Cognitive Advisors for Enhancing Product Item Data2206 would be layered into a progressive revealing item detail page onthe mobile experience.

In some embodiments, the system is configured to prioritize theCognitive Advisors and their recommendations to fit the mobile userexperience in a prioritization module 2210. Special Cognitive Advisorsettings enable configuration of Cognitive Advisor Pipelines 2208specifically for mobile device access. i.e. specific Cognitive AdvisorPipelines may be turned on or off for access from a mobile device.Within these mobile Cognitive Advisor Pipeline settings, specificcognitive advisors can be turned on or off within a given pipeline, whenaccessed from a mobile device. Furthermore, the specific cognitiveadvisors within a pipeline may be prioritized with a Cognitive AdvisorPriority setting as 1|2|3 priority order, 1 being highest and mostimportant. The Cognitive Advisor Priority setting causes the cognitiveadvisors to be prioritized in their execution in the backend processingpipeline—higher priority cognitive advisors are executed first. TheCognitive Advisor Priority setting further causes the recommendationstream coming from the backend processing pipeline to be tagged with thesame priority setting. The mobile app interface adapts the display ofrecommendations coming from the Cognitive Advisor Pipelines based on thepriority setting tagged with each recommendation. Highest priorityrecommendations are provided first, since these are the ones most likelyto meet the user expectations. The screen display is managed to presentpriority level 1 recommendations first, with a progressivescrolling/swiping interface mechanism to reveal further recommendationswithin the available screen real estate on the mobile device.

Mobile Adaptive Navigation

An intelligent browse experience can be tuned for mobile access. Theuser's context, past browsing, past product selections and pastpurchases are further used to tune the intelligent product browsingexperience for higher relevance in the mobile environment.

FIG. 23 shows an example mobile adaptive navigation. In someembodiments, the following parameters are additionally taken intoaccount to augment the user's context (which already includes theirdepartment/division, their geographic region of their business location,their individual role, their purchasing permissions etc.): the user'spast browsing patterns with an emphasis on recent browsing patterns; theuser's past product selections with an emphasis on recent productselections; the user's past cart additions with an emphasis on recentcart additions; the user's past requisitions with an emphasis on recentrequisitions; the user's past purchases with an emphasis on recentpurchases; the user's current geographical location. These parameters gotowards establishing the Adaptive Navigation Mobile Context 2302 or theuser. An intelligent browsing interface is provided in the mobile appbased on the Adaptive Navigation 2304 backend service, as discussed inSection 3.1. The Adaptive Navigation Mobile Context is layered on top ofthe adaptive navigation structure provided by the Adaptive Navigationbackend service. The result of the layering of the Adaptive NavigationMobile Context is that the category/subcategory ordering is furtherranked/prioritized using these parameters. The individual products shownat any given category/subcategory node in the Adaptive Navigationtaxonomy structure are further ranked/prioritized using theseparameters.

Mobile Requisitions and Orders

In a conventional Web-based Procurement interface, when the productselections are ready in the cart, a requisition can be made from itdirectly in the system and sent for approval (assuming those requisitionand approval steps are turned on), or the cart is transferred to theoriginating ERP system where the requisition and approval flows happen,and orders are suitably generated.

FIG. 24 shows an example data flow for mobile requisitions and orders.In some embodiments, in a mobile environment, after product selectionsare made in the mobile device, and the cart is ready, a few potentialend points for the cart would be as follows. The first end point is“save for later”: the direct log-in user credentials are linked to theERP credentials, so a user could direct log-in through the mobile webpage and then log-in and view the same items in their cart through theire-procurement user. The second end point is “assign cart”: the user canassign their cart to an e-procurement user for ordering. The third endpoint is “PCard”: the user can check out their cart for Suppliers thatare PCard enabled.

The system is configured to know based on how the user has authenticatedand how the supplier is configured to do business, or which potentialcart outputs are available. A unification of account credentials willallow for a direct log-in user to manage their account. This allows forportable shopping that is still configured to integrate with thecustomer's ERP system at a later date.

In addition, a workflow of cart 2402/order 2404 assignment can beperformed for cart and procurement rule validation 2406 before passingto a central buyer user for ordering through the ERP procurement system.

Further order tracking history and updates are gathered from API callsto the ERP system and gathered from Purchase Order 2408 update documentsto keep the mobile user fully informed throughout the process inreal-time.

Real-time items can be purchased from a mobile application and stay ‘insync’

Real-time items can be validated to be price accurate at the time oforder—not just at the time of shopping. With a fair number of itemstaking upwards of a full business week to be approved through theexisting Requisition Approval process, stale data causes PO/Invoicemismatches that cost companies a great deal of lost profit in the formof (1) making poor buying decisions on stale data, (2) change ordermanagement as items fall to backorder while waiting approval (3) addedresource cost from AP to confirm and correct the mismatch and (4) missedopportunities for invoicing discounting for prompt payment caused byapproval delays.

Mobile Compliant Purchasing in External Stores

FIG. 25 shows an example data flow for mobile compliant purchasing inexternal stores. FIG. 26 shows another example data flow for mobilecompliant purchasing in external stores. In some embodiments, when auser visits brick and mortar stores, to fulfill their purchasing needs,today, such product selections and purchases made in store, couldrepresent non-compliant spend from a procurement perspective, and inturn, could be a contributor for savings leaks. This mobile procurementsystem is configured to enable compliant purchasing in external storesas follows. The user can scan a given product's bar code using themobile procurement tool, and have the system automatically convert thatinto a universal search in the marketplace for the exact product ifavailable, and/or similar products that are contracted by procurement.The user can take a photo of a given product and have the system do animage match on known contracted products to present the exact productfrom a contracted supplier. In the event there is not a product matchfrom the provided photo, the system will proceed to create a smart querybased on the product type and attributes gleaned from the photo and runa universal search across contracted suppliers to find similar products.

In a first option, the client be given access to a myriad of retailecommerce sites that integration into the B2B mobile platform 2502.These items can be searched for or used for comparison purposes inreal-time against existing contracts. More specifically, a user shops onthe public web for an item of their choosing. With an app enabled ontheir mobile or desktop browser, the user will be notified in real-timeof alternatives from other retail providers as well as the company'scontracted suppliers providing hosted catalogs 2504 or punchout catalogs2506. Alternatively, users logging into their company's B2B Marketplace2602 can tap into the competitive knowledge of all of their company'sstrategically sourced supplier content and compare it for shopping andanalytics purposes to various retail ecommerce sites in real-time toensure the user is always aware of the best price and availability tothem at their decision point.

In a second option, the client user can shop as they wish and install amarketplace bookmarklet to track in real-time similar items and cheaperpricing from their unique, contracted pricing library of millions ofitems.

In a third option, in the real world, the user can use their camera in abrick-and-mortar and search the mobile marketplace for a similar imageor product key (part number). The mobile procurement system isconfigured to utilize an existing image web comprising of stock imagesand consumer-uploaded images to find exact and similar contracted imagesand item master of enriched product attributes, providing users areal-time comparison for purchasing.

Mobile Personal Expense Reporting

FIG. 27 shows an example data flow for mobile personal expensereporting. In some embodiments, when a user visits brick and mortarstores or even online stores for purchasing products they need, thisspend may be considered non-compliant by procurement, but still cannotbe controlled. In turn, as a first step, procurement organizations want,at a minimum, to know what spend is occurring in what category andcontext. To this end, users do have to provide detailed expense reports.The mobile procurement tool simplifies this process by letting a user dothe following. A user can paste an electronic receipt received from anexternal store, and the system is configured to intelligently extractthe details needed. The user can also scan using a camera 2702 of acellphone 2708 a printed receipt 2704 received from an external store,and the system is configured to work from the digitized receipt image toextract the details needed. In addition, the user can talk via amicrophone 2706 of the cellphone 2708 to provide the receipt informationin natural voice, and the system is configured to extract the detailsneeded. The system is then configured to create an expense report forsubmission, and file the same into the procurement system as an artifactof external spend.

In some embodiments, in the background, the system can be configured toutilize the mobile camera to translate receipts into expense reportitems. The system is configured to also utilize the expense report toretroactively inform the procurement system/team of missed opportunitiesfor savings of items 2710 that did not leverage contracts but couldhave.

The system can also be configured to take voice commands in naturallanguage to allow for quick, hands-free entry of their expense item forunreadable items from the camera. The system can be configured toutilize Natural Language Processing skills and algorithms to interpret,categorize, and compare the purchase to potential available alternativesfor analytics and future gamification applications 2710 to enticebeneficial behaviors in future endeavors. For goods,

In some embodiments, for goods, the analytics can include identifyingthe item(s) by part number and comparing the price purchased vs.contracted items, and identifying the item(s) purchased and groupingthem for future negotiations for improved bulk pricing if there is anoticeable amount of spend without contract. For services, the analyticscan include identifying the location and types of services expensed foroptimization of spending and prior-purchase intelligence fornegotiation.

Mobile Voice Skills for Procurement

The following are examples of the kinds of voice skills that would besupported by the mobile procurement tool to work with personal voiceassistants like Alexa, Google, Cortana and Siri: reorder a product (thathas been ordered before by this user, using part # or product name anddetails); order a new product (using part #, or specific product nameand details)—this would use past purchases made by this users and othersin the organization to increase the precision of the response; check onthe status of a requisition; check on the status of an order; approverequisitions received; check on spend levels; check on savingsactualized; check on availability of a product.

Many more voice skills of this kind will support a modern voice-basedinteraction with purchasing and other procurement needs using devicesbased on personal voice assistants. For example, the system can beprogrammed to utilize existing Alexa skill formulations, tapping intopatented real-time framework through this new utilization method ofdelivery of real-time content to clients. The system can also beprogrammed to utilize push notifications from the real-time system tothe various enabled devices will allow for notifications of updates topricing, shipment, and availability to the appropriate users inreal-time.

Mobile Conversational Chatbot for Procurement

In some embodiments, the system is programmed to provide aconversational chatbot for use on an electronic procurement system.Example conversations configured for such a chatbot are shown in thetables below.

First Example

-   -   a) User: Hey Computer. Find me a wireless keyboard and mouse        combo.    -   b) Computer: There are five products from preferred suppliers,        and hundreds from the open market. And your peers have purchased        two products in the last month. How would you like to choose?    -   c) User: What are the two products purchased by my peers?    -   d) Computer: First, there is the Logitech Wireless Keyboard and        Mouse combo for $49 and this has been purchased 4 times in the        last month. Second, there is the Bionics Wireless Keyboard and        Mouse combo with free laser pointer bundle for $59 and this has        been purchased 6 times in the last month.    -   e) User: Ok. Order the Bionics one, as I could use a laser        pointer as well.    -   f) Computer: Got it. I've added the Bionics Wireless Keyboard        and Mouse combo with free laser pointer bundle for $59 to your        cart. Is there anything else you want to buy now?    -   g) User: No. This is it.    -   h) Computer: Got it. I've created a requisition for this and        dispatched it for approval. I'll keep you updated when your        order is placed and when it becomes available.    -   i) User: Thanks!

Second Example

-   -   a) User: Hey Computer. How many units of EX456732B are        available?    -   b) Computer: There are 3 units available from Macon and 2 units        available from Directus.    -   c) User: I will need 10 units by end of next week.    -   d) Computer: Got it. Shall I order these five units available        now?    -   e) User: Yes. And place a quote with these two suppliers for        five more to be available in time.    -   f) Computer: I've placed an order for 3 units of EX456732B from        Macon and 2 from Directus. And I've sent a quote for 5 more from        them. I'll keep a watch on follow up from them and notify you.    -   g) User: Thanks. Maybe it will help if you can look outside our        marketplace for other suppliers who may have this in stock!    -   h) Computer: Yes, I'm checking now . . . [pause] . . . I've        found that MicroUnlimited has 7 units and Temporum has 3 units.        We haven't sourced from either of these suppliers before.    -   i) User: What are their ratings?    -   j) Computer: I'm checking D&B, BBB and SupplierCentral now . . .        [pause] . . . MicroUnlimited has a top BBB rating, no complaints        on D&B, and rated 9.2 out of 10 on SupplierCentral. Temporum has        a top BBB rating, 1 complaint on D&B for a code violation, and        rated 7.6 on SupplierCentral.    -   k) User: Go ahead and send a vendor invitation notice with a        quote for five units to MicroUnlimited and ask them for an        expedited response.    -   l) Computer: Ok. I've sent out the vendor invitation with the        quote and request for expedited response by Monday next week. Is        there anything else?    -   m) User: No, that's it for now!

Third Example

-   -   a) User: What is the status of my order on the desktop        oscilloscope?    -   b) Computer: The order was dispatched to ScienceDepot five days        ago. We received an order confirmation from ScienceDepot        yesterday.    -   c) User: What is the contract turnaround time with them for this        category?    -   d) Computer: Their contract says that they should deliver this        category of standard, non-customized Lab Equipment within five        days.    -   e) User: Is there a penalty clause in their contract for late        delivery? By the way, how have they been performing in the past        on delivery times?    -   f) Computer: No, there is no penalty clause in their contract        for late delivery. They have been trending down on delivery        times, and have been consistently late between 10% and 45% on        their last 10 orders from us.    -   g) User: This is not good. Any idea if they are having        operational trouble?    -   h) Computer: I'm checking on this now . . . . [pause] . . . The        Baltimore Sun reports that they are having a major        organizational shake up with the CEO and CFO leaving the        company, after fraud complaints were made by their customers.        Justia lists two lawsuits against them.    -   i) User: What is their contract termination clause?    -   j) Computer: We need to give them 7-day notice for cause.    -   k) User: Who are other suppliers in this category that we could        source from?    -   l) Computer: We already have five competitive suppliers in our        system in this category.    -   m) User: In that case, go ahead and remove them from our        configuration, and send them a termination notice with their        delivery performance as the cause.    -   n) Computer: I have removed them from our configuration. And I        will send them a termination notice and let you know when that        is dispatched. What should I do about the current order open for        the desktop centrifuge?    -   o) User: Wait for the order to come through in the next three        days, and if it doesn't come through, cancel the order, and        order it from one of the other suppliers.    -   p) Computer: Ok. I will keep the order open for three days. And        if that isn't fulfilled, I'll place the order with Measurement        Equipment Central.    -   q) User: Sounds good.

It should be understood that any one or more of the teachings,expressions, embodiments, examples, etc. described herein may becombined with any one or more of the other teachings, expressions,embodiments, examples, etc. that are described herein. Thefollowing-described teachings, expressions, embodiments, examples, etc.should therefore not be viewed in isolation relative to each other.Various suitable ways in which the teachings herein may be combined willbe readily apparent to those of ordinary skill in the art in view of theteachings herein. Such modifications and variations are intended to beincluded within the scope of the claims.

Having shown and described various embodiments of the present invention,further adaptations of the methods and systems described herein may beaccomplished by appropriate modifications by one of ordinary skill inthe art without departing from the scope of the present invention.Several of such potential modifications have been mentioned, and otherswill be apparent to those skilled in the art. For instance, theexamples, embodiments, geometrics, materials, dimensions, ratios, steps,and the like discussed above are illustrative and are not required.Accordingly, the scope of the present invention should be considered interms of the following claims and is understood not to be limited to thedetails of structure and operation shown and described in thespecification and drawings.

3.4. Real-Time Cognitive Advisor Framework for Procurement

Disclosed herein is an Extensible Framework for Enhancing MarketplaceExperience and Procurement Efficiency and Effectiveness in Real-timeusing AI, Predictive Modeling, Behavioral Intelligence, Big Data andCollective Intelligence.

Private marketplaces typically used in Procurement systems function withparadigms that are a decade or older and leave much to be desired interms of user experience, efficiencies, effectiveness, purchasingcompliance and savings. A system is disclosed that enables organizationsto deploy private marketplaces with real-time universal search acrossall content including content from punchout websites and forms afoundation for driving greater user satisfaction and higher purchasingcompliance. By identifying the right sourced suppliers and opensuppliers, the organization can use the system to derive more savingsfrom their purchases. Collective intelligence comes in the form of theorganization's product knowledge as expressed in reviews and ratings onsuppliers, reviews and ratings on products, collaborative discussionsincluding in question boxes associated with a given product, productselections in cart and in purchases made. This wealth of knowledge canbe tapped to drive better purchasing and sourcing decisions.

Behavioral intelligence comes in the form of being able to follow users'searching patterns and tracks and choices to better model what they aretrying to achieve and lends itself to innovations that can lead users towhat they are looking for faster. Big data comes in the form of themassively increasing collection of inter-related data between suppliers,products, selections, purchases, buyer users and more. Anonymized forsafe consolidation across organization boundaries, this is a growingsource of intelligence that can help drive context, analytics andbenchmarking to drive better decisions. Predictive modeling applied tothe dynamic elements of the marketplace such as pricing trends,purchasing trends, supplier reliability and competitiveness and more,can bring another dimension of value to improving purchasing decisions.

The use of AI to create and deliver intelligent insights in the rightcontext in real-time, enabled a wide range of innovations to augment theprocurement experience overall. A Cognitive Advisor Framework, asdetailed in this document, has substantial impact on procurement, fromthe quality of the search experience, to the efficiency gains in productselection, to the reliability of product and supplier selection forpurchase, to streamlining repeated purchases, to maximizing savings byconsidering the myriad factors involved. The open, extensible frameworkpresented here may be used to permit the continuous creation and use ofmany different targeted cognitive advisors over time that enrich theentire procurement experience and value to the organization.

An example Cognitive Advisor Framework, shown in FIG. 28, bringsintelligent insights into purchasing decisions. The framework can takeinputs from different contexts and provide recommendations suitable forthe context. A wide range of data assets 2802 ranging from productinformation, product knowledge, product preferences, pricing history,product ratings and reviews, to cart selections, purchases, contracts,supplier ratings, user behavior, competition and more are available forthe Cognitive Advisors to tap into to bring intelligence and insights topurchasing and sourcing decisions.

In some embodiments, Specific Cognitive Advisors 2804 (e.g., independentmodules) can be plugged into the Framework via configuration in aprocurement setting for a given organization. The Framework may beresponsible for real-time orchestration and execution of a pipeline ofCognitive Advisors in parallel, or in specific sequence as needed. TheFramework may stream real-time updates and recommendations as available.

A Cognitive Advisor may be implemented as a focused component thatderives intelligence and insights using AI/machine learning/predictivemodels or similar approaches, from specific slices of data from the datauniverse, to deliver specific insights or recommendations orintelligence to improve purchasing or sourcing or other decisions. ACognitive Advisor may identify itself as service for a specificcontext/purpose. These include, for example: a specific CognitiveAdvisor may work on a given query and context as input and providerecommendations 2808 on certain specific products to be presented abovethe fold, a different specific Cognitive Advisor may work on a singleproduct item 2810 and give specific recommendations 2812 to be presentedalongside that product item, another specific Cognitive Advisor may beprovided with a single user context as input and provide auto-orderreminders, or watched products which become available, or a list ofspecific recommendations to attend to in their personal dashboard, and adifferent Cognitive Advisor may be tuned to work on behalf of theprocurement buyer in helping them get predictive insights into savingsactualization, savings leaks and more, in their personal dashboard.

In turn, specific chains of Cognitive Advisors could be activated andexecuted in real-time depending on the actual usage of the procurementsystem and marketplace by the buying users and suppliers.

The data assets available are organization specific as well as anoverall big data set consisting of suitably anonymized data assetsintelligently merged across organizations. In some embodiments, this maybe used to allow Cognitive Advisors to not only provideorganization-specific insights in specific granularities, but alsoprovide benchmarking/comparisons and related insights from across theentire set of organizations served by the system.

Query results in procurement marketplaces can be significantly enhancedusing specific Cognitive Advisors that provide result recommendationsfor a given query and user context. FIG. 29 illustrates the use of theCognitive Advisor Framework with three configured Cognitive Advisors2902. It is activated with the Cognitive Advisor Framework Type of“Query result recos” 2904. The input provided is a user's query 2906 on<hammer drill> and their entire query context 2908 including suppliermix, supplier preferences etc., as well as the entire user context 2910including their id, their division/department/region, etc. Theconfigured Cognitive Advisors return a stream of result recommendations2912 pertaining to this input context as their output. The userinterface can provide “cognitive recommendations” as shown in FIG. 30.

This approach provides a number of advantages. For example, of theconfigured Cognitive Advisors, for a given query, zero or more of themmight return recommendations. In turn, the user interface for thedisplay of Cognitive Recommendations may not be present (if zerorecommendations were made for a given query), OR, as illustrated in FIG.30, many Cognitive Advisors may present recommendations. In this chosenexample, we can see that “Best Bets” Cognitive Advisor 2914 returned 2recommendations 2916, the “Bundles” Cognitive Advisor 2918 returned 1recommendation 2920, and the “Savings Max” (the Savings Maximizer)Cognitive Advisor 2922 returned 1 recommendation 2924. The illustrationin FIG. 30 employs a horizontal “accordion” type user interface gadget3002 which displays the three Cognitive Advisors in tabs that expand,and contract left to right, occupying the available display space forrecommendations one at a time. The Cognitive Recommendations wouldstream back into the user interface handler in real-time, so theinterface can dynamically present the highest quality recommendations tothe user as soon as they are available. Any number of availableCognitive Advisors may be configured into the Cognitive AdvisorFramework. Here are some specific examples in this innovation.

An example “Organization Preferences” Cognitive Advisor may mine one ormore of the following: all product items placed into the cart, and thecontexts in which they were made, by one or more users, across one ormore departments/divisions/regions within the organization (“CartSelections”), all product items placed into actual orders sent to one ormore suppliers (“Purchases”), the users and their contexts from whichthese Cart Selections or Purchases were made, the price, the timestamp,the category and other relevant product information, product knowledge,product preferences, product ratings and reviews, supplier history,contracts, supplier ratings and user behavior.

Starting with the input context (query, query context, user contextetc.), this Cognitive Advisor does intelligent matching to find up tothree high quality product item recommendations that match the intent ofthe user's query. In the example illustrated in FIGS. 29 and 30, the“Organization Preferences” Cognitive Advisor, if configured, may havespotlighted up to 3 hammer drills preferred by the organization, thatare relevant to the user and the user context. The following are severalnoteworthy elements of the matching algorithm: purchases are a strongersignal than Cart Selections for the organization's preference of acertain product item for a certain family of queries whose intent is tofind such item, frequency of Purchases and Cart Selections are someimportant criteria for determining the rank ordering of potentialrecommendations, leading to up to three qualified recommendations forthe user, and the user context is an important element in the matchingalgorithm. For example: A user in Marketing department wanting to find adigital tablet for creating digital illustrations is more likely to findpeer selections (i.e. selections by others in Marketing) to be morereliable than, say, that of an incidental selection by some otherselsewhere in the organization.

This example Cognitive Advisor will be returning a Recommendation Scorefor each item, presented on a scale of 0-1. The user interface maychoose to display such Recommendation Score for an item via a bardisplay, OR may choose to map the Recommendation Score to a discreterange of Low Relevance to Medium Relevance to High Relevance, OR maychoose to not display that while still using such Recommendation Scoreto rank order the Cognitive Recommendations. For example, FIG. 30 doesnot display the internally available Recommendation Score for eachrecommendation.

For each item recommended, this Cognitive Advisor also provides anevidence stream, that can be used to answer user questions such as: “Whyam I being shown this recommendation?” In turn, this enablestransparency to the recommendation, and will engender user trust in therecommendation. For example, FIG. 30 illustrates this by including a“Why” link 3004 with each recommendation.

In some embodiments, an Organization Preferences Cognitive Advisor mayalso have a learning engine inside. This learning engine learns userbehavior from actions taken by users to whom recommendations from thisCognitive Advisor have been delivered. These actions may include, forexample, that a user clicks on an Organization Preferencerecommendation, a user selects an Organization Preference recommendationfor adding to cart, or a user selects an Organization Preferencerecommendation for adding to cart followed by an actual purchase. Theentire query context and user context are taken into account, along withthe signals above of user actions from Organization Preferencesrecommendations, for learning and improving the quality of therecommendations made to all users overall within the organization. TheOrganization Preferences Cognitive Advisor taps into the CollectiveIntelligence of the organization on product selections and purchases, toprovide high quality, reliable recommendations for the user doing thequeries.

The disclosed system may implement a “Best Bets” Cognitive Advisor 2914.Private Procurement Marketplaces that are configured to work with a richselection of suppliers or even the long tail of suppliers end up with aglut of products to pick from, to make a purchase. A buyer user can bepresented with large numbers of products for specific queries. Multiplestrategies are deployed to help users navigate this, including facetedfilters, real-time clustering and visual navigation for product searchresults, categorization, and more.

The innovation presented here is the inclusion of a “Recommend as BestBet” (or simply “Recommend this”, or similar variation), link/button toeach product listed in the universal search result set. A buying usercan click on this link/button to suggest that this item be recommendedas a Best Bet for this query and similar queries (similar queries arebuilt out using a similarity measure on the queries done so far).

When a user clicks on “Recommend as Best Bet”, their recommendation iscaptured along with the complete query and buyer context and relatedsettings, as an input for a Best Bets learning engine. In an exampleimplementation, the “Best Bets” Cognitive Advisor 2914 recommends agiven previously recommended item for any user in the same department,in the same region as the recommending user. Additional signals of thestrength of the recommendation can come from, for example: item choicefrequency in cart selections, item choice frequency in purchases, andpurchase frequency will be weighted more than Cart frequency.

The Best Bets learning engine learns user behavior from actions taken byusers to whom the Best Bets recommendations have been delivered. Theseactions may include, for example, that a user clicks on a Best Betrecommendation, a user selects a Best Bet recommendation for adding tocart, or a user selects a Best Bet recommendation for adding to cartfollowed by an actual purchase. The entire query context and usercontext are taken into account, along with the signals above of useractions from Best Bets recommendations, for learning and improving thequality of the recommendations made to all users overall within theorganization

A Best Bets Cognitive Advisor 2914 may be a superset of the OrganizationPreferences Cognitive Advisor. In some embodiments, either one can beturned on within the Cognitive Advisor Framework, depending on thebuying organization's choice. In addition, the ranked placement of itemsin Best Bets for a given query in a given category in a given region,can be additional data for the Global Item Master, potentially drivingrecommendations like this or other features across company selections.

The disclosed system may implement a “Bundles” Cognitive Advisor 2918.Buyer organizations can configure a bundle of products to make it easyfor making purchasing decisions in specific use cases. For example, a“New Employee Bundle”, might include a specific swivel chair, a specificlaptop, docking station, two desktop flat panel monitors, a laptop bag,extended life battery, wireless keyboard and wireless mouse.

An electronic procurement platform may search across configured bundlesin the context of universal search across hosted catalogs, punchoutsupplier websites, bundles, order guides, inventory and more. The“Bundles” Cognitive Advisor 2918 may take this type of bundle searchingto another level of value by taking into account the user context andthe query context to create a more sophisticated matching algorithm togenerate Bundles Recommendations.

The learning engine underneath the Bundles Cognitive Advisor 2918 alsolearns user behavior from actions taken by users to whom the Bundlesrecommendations have been delivered. These actions may include, forexample, that a user clicks on a Bundle recommendation, a user selects aBundle recommendation for adding to cart, or a user selects a Bundlerecommendation for adding to cart followed by an actual purchase. Theentire query context and user context are taken into account, along withthe signals above of user actions from Bundles recommendations, forlearning and improving the quality of the recommendations made to allusers overall within the organization. In addition, the highly activebundles for a given query in a given category in a given region, can beadditional data for the Global Item Master, potentially drivingrecommendations for new Bundle creation or Bundle enhancement for thisorganization or other organizations overall.

A system implemented based on this disclosure also implements a “SavingsMaximizer” Cognitive Advisor 2922. Procurement organizations tend tohave various ways for generating Savings from sourcing differentsuppliers. Some may get a flat x¾ off list price discount. Some may geta discount range from x¾ to y¾ depending on tiers of volume ofpurchasing with the given supplier. Some may get cashback from suppliersbased on the volume of purchasing done with them.

In turn, this creates a peculiar situation where sometimes, the lowestprice product listing from a given supplier, or even a given Best BetsCognitive Advisor 2914 or other Cognitive Advisor recommendation(s) maynot be the right choice for the user to make, if the organization'sstrategy is to maximize overall savings (versus maximize savings on aper-transaction basis), or to optimize supplier mix. What is needed is agreater sophistication in the Procurement system to gather informationon the organization's procurement strategy. With structured knowledge ofthe procurement strategy (overall or on a per-category basis, dependingon how the organization has configured the strategy), and a structuredunderstanding of the contract details (including discount structure andtiers), an intelligent capability would make recommendations thatmaximize savings overall.

The “Savings Maximizer” Cognitive Advisor 2922 takes a strategic view ofoverall savings, based on the procurement strategy, supplier contracts,current search query and recommendations and items displayed, savingstargets, savings to date, current spend down to a given category andgiven supplier, and makes recommendations on product selection from agiven supplier that would maximize overall savings.

For example, a buying organization has a contract with TechCentral forIT Supplies. The savings are based on tiers of buying volume. Tier 1Savings: 3% off contracted price for volume up to $100 k; Tier 2Savings: 3.5% off contracted price for volume from $101 k-$250 k; Tier 3Savings: 4% off contracted price for volume from $251 k-$500 k; Tier 4Savings: 5% off contracted price for volume from $501 k-$1 M; Tier 5Savings: 6% off contracted price for volume over $1 M+.

When a given user is performing a search in the IT Supplies category,and is adding, say, a color laser printer from TechDepot, anothersupplier in IT Supplies, for $4500, which is the best listed price amongall available choices at that time. The same color laser printer fromTechCentral may be $4750, clearly not attractive for the user at thattime. However, the Savings Maximizer Cognitive Advisor 2922 tracks andcomputes the fact that buying this item from TechCentral (for aseemingly higher item price) would bump the spend from Tier 4 Savings toTier 5 Savings. This in turn, would drive the overall savings higher forthe organization. Based on this the Savings Maximizer Cognitive Advisor2922 would send along a recommendation to the buy the color laserprinter from TechCentral.

Additional benefits delivered from the Savings Maximizer subsystem iscapability to power personal spend and personal savings targets,department/division/region spend and savings targets in an informationinterface, updated in real-time. Further benefits delivered build onthese analytics and constructs a gamification interface which enablessocializing savings objectives and engage users in achieving higherlevels of savings overall.

Other Cognitive Advisors can be implemented. For example, a “QueryRewriter” Cognitive Advisor may be implemented. Users can sometimes findit difficult to formulate query terms that express the purchase need.The Query Rewriter Cognitive Advisor mines past queries from all usersand the corresponding cart selections and purchases, to create asemantic model of the product universe of interest to the organizationand uses the same to provide a better query to consider using.

A “Smart Guide” Cognitive Advisor may also be implemented. In additionto a Universal Search based Marketplace, there could be other toolsenabled for the users to use. For instance, one or more tools forcustomizable products may be configured, a tool for creating quotes maybe configured, one or more specialized tools may exist only on certainsupplier punchout sites, and more. In this setting, it may be beneficialto guide the user to the right tool based on their query and context.The Smart Guide Cognitive Advisor can look at available configurationinformation on tools, past paths taken by users from the point ofexpressing a need to the point of getting something added to the cart,and auto-recommend the use of one or more tools suitable to the currentuser's need. For complex buying needs, this Cognitive Advisor mightconnect the user with a Procurement Buyer in real-time to help meettheir needs. Such a Cognitive Advisor may dramatically improve theoverall user experience with the system by guiding them to the righttool or person for their need.

A “Result Template” Cognitive Advisor may also be implemented.Sometimes, the user's query is very specific like a part number, orreasonably specific like a particular brand, model and attribute set. Atother times, the user's query is very general like <gloves> or<printer>. At still other times, the query can be for non-productinformation like just a supplier name, example, <msc>. A wide variationexists in the query diversity seen in Procurement contexts. Depending onthe type of query, the user experience can be dramatically enhanced byorganizing the right type of content into the right type of informationlayout. For instance, for general queries like <printer> or <gloves> itis very likely that the user might need broader information,organization selections, and other products from suppliers. In turn, aResult Template Cognitive Advisor might analyze the user's query andcontext and suggest a suitable result template along with otherparameters such as the sources of information and their layout withinthe template. The user interface can use this information to createtailored information layouts to best communicate the right informationfor the intent of the user's query.

A “Real-time Path Optimizer” Cognitive Advisor may also be implemented.Users may sometimes take multiple steps from the start of their querybefore they find what they are looking for, or sometimes they may betrying to pull together multiple product items into a cart, or sometimesthey try a given path through searching and abandon it to visit thepunchout site of a given supplier. The Real-time Path OptimizerCognitive Advisor watches the activity of the user in atomic, granularsteps, in real-time, and provides optimized path recommendations thatcould be rendered into the user interface in the form of real-timeguided navigation steps for the user. For example: As the user beginsadding more than one item to the cart, this Cognitive Advisor canrecommend, based on its learning, the set of items that could ofrelevance for the user to consider adding to the cart.

One or more of the disclosed Cognitive Advisors may be implemented andmay provide features for enhancing a product item and associatedinformation. Product items displayed in procurement marketplaces can besignificantly enhanced using specific Cognitive Advisors that provideresult recommendations for a given item, category, supplier and usercontexts. FIG. 31 shows an example configuration of the CognitiveAdvisor Framework for enhancing product items, using five uniqueCognitive Advisors 3102.

These Cognitive Advisors may be triggered with a type “Product itemrecos” 3104. The input context provided may include one or more of: anindividual product item 3106 (i.e., an item in search results, but alsoin any other desired context where a product item is in focus), querycontext 3108 which may have originated the product item, and usercontext. Each Cognitive Advisor that can enhance an individual productitem may produce different information nuggets and/or actions for a userto take. FIG. 32 shows the example of five configured Cognitive Advisorsfor enhancing product item information, and how the five configuredCognitive Advisors specifically lead to enhanced item details andactions 3202, 3204, 3206, 3208, and 3210 shown to the user.

An “Auto Buy” Cognitive Advisor 3110 may also be implemented in thedisclosed system. Users often buy some products with a certainregularity. Example: Paper, Printer Ink Cartridges, Consumable OfficeSupplies etc. The Auto Buy Cognitive Advisor 3110 mines the patterns ofa given user's purchases to predict the typical frequency of repeatpurchase of certain products. An Auto Buy facility will enable a givenproduct to be automatically ordered at given frequency. The Auto BuyCognitive Advisor 3110 can recommend that the user should consideradding the given product (seen presumably in a query result set) to theAuto Buy list.

A “Supplier Choices” Cognitive Advisor 3112 may also be implemented inthe disclosed system. Within a given query result set, it is possiblethat the very same product item may be available from differentsuppliers (perhaps at different terms or prices or with differentoptions). The Supplier Choices Cognitive Advisor 3112 mines the searchresult set and cart selection data from all users in the organization topresent a short list of Suppliers (with links to the very same productitem at those supplier sites or catalogs) to the user.

A “Similar Items” Cognitive Advisor may also be implemented in thedisclosed system. There may be a set of similar product items availablefrom the same or different suppliers, that are truly comparable. TheSimilar Items Cognitive Advisor extracts the brand, model, attributesand also mines the result set, the cart selections, past purchases andthe global item master (if available) and presents a recommendation ofup to three top items similar to the given product.

An “Item Enrichment” Cognitive Advisor 3116 may also be implemented inthe disclosed system. Product item data quality varies widely acrossdifferent suppliers, often missing key attribute information or evendescription, images, videos, specifications and the like. The ItemEnrichment Cognitive Advisor 3116 mines the result set, the cartselections and past purchases, and the global item master (if available)and presents a recommendation of the specific aspects of the productitem data that should be enriched. The benefit of doing so is to enablea user to work with the richest product item data available, to makebetter purchasing decisions.

A “Pricing Intelligence” Cognitive Advisor 3118 may also be implementedin the disclosed system. The phenomenon of price variation over time andacross suppliers presents the real possibility that lower prices mayexist for products being considered by a user during their search. Inturn, several different approaches exist to monitor price trends andcreate price predictions. The Pricing Intelligence Cognitive Advisor3118 taps into historical prices across products in different categoriesand recommends whether the user should buy now, or wait a given period,along with the confidence measure in its prediction.

Other Cognitive Advisors may be implemented in the disclosed system toEnhance Product Item Information. These may include, for example, a“Product Reviews” Cognitive Advisor. This Content Advisor taps intoorganizational, cross-organizational and public reviews around a productand provides a distilled summary of the top positive and top negativereviews on the product, as a recommendation to be presented with theproduct item.

Another example is a “Product Risk” Cognitive Advisor. Products may havequality issues, performance issues, or may have product claims notactually delivered by the product. Typically, this sort of feedback onproduct can be found in product reviews and product ratings within theorganization, across organization boundaries, as well as online on manyproduct complaint/feedback sites, online stores and even social media.While the data around product risks is out there, it is near impossiblefor a regular user to find it in a timely and consumable manner. TheProduct Risk Cognitive Advisor taps into organizational andcross-organizational sources of product feedback, and also productreturn patterns evidenced through the procurement system, and creates asimple, visual, consumable risk profile and risk rating, and returnsthat as a recommendation on a given product item.

Another example is a “Supplier Reviews” Cognitive Advisor. Taps intoorganizational, cross-organizations and public reviews around a supplier(or manufacturer) and provides a distilled summary of the top positiveand top

Another example is a “Supplier Risk” Cognitive Advisor. For select, highvalue products such as high-end devices, important or critical labsupplies, customized products, and overall non-commodity products, thereis a risk profile around the supplier (and manufacturer) concerningviability, reliability, reputation and more. And in general, in a globalsetting, awareness of environmental factors, social-governmentalfactors, legal factors become increasingly important. There are vastpools of disparate information sources out on the public Internet andacross specialized databases (blacklists from governmental sources,legal databases etc.), but it is nearly impossible to find informationabout risk on a given supplier (or manufacturer) in a timely andconsumable manner. The Supplier Risk Cognitive Advisor taps intoorganizational and cross-organizations sources of product feedback andalso collaboration and information exchanges with a given supplierincluding support request trails, and creates a simple, visual,consumable risk profile and risk rating, and returns that as arecommendation on a given product item.

One or more Cognitive Advisors may also be implemented for Enhancing aUser's Experience. In addition to enhancing query results, or productitem data, Cognitive Advisors can help enhance the overall userexperience within a Procurement Marketplace and system. These couldenhance a user's experience through their personal dashboard, the user'scart as they manage them, the user's compare tool and more. This mayinclude, for example, an “Auto Order Reminder” Cognitive Advisor. WhenAuto Orders are used by a given user, this Cognitive Advisor creates theright requisition and places timely reminders to have the user confirmthe repeat purchase.

Another example is a “Watched Products” Cognitive Advisor. The user canplace products on a watchlist to get info on availability of one or moreunits, from one or more suppliers. This Cognitive Advisor placesactionable notifications of the availability of the watched productswhen they are available from a given supplier.

Another example is a “Price Change” Cognitive Advisor. The user canplace products on a price watchlist to get info on price shifts, basedon threshold and rules specified, from one or more suppliers. ThisCognitive Advisor places actionable notifications when the specifiedrules are met, along with broader Pricing Intelligence on the givenproduct(s).

Another example is an “Auto Approve” Cognitive Advisor. If approvalfunctionality is turned on in the Procurement Marketplace, where theuser is responsible for approving purchase requisitions from others, theapproval rules can be automated based on provided rules, as well as pastapproval patterns learned by a learning engine. When turned on inconfiguration, the Auto Approve Cognitive Advisor, will automaticallyconduct approval steps as appropriate and notify the user.

Another example is an “Auto Requisition Reminder” Cognitive Advisor.When a user makes requisitions, and approval functionality is turned onin the Procurement Marketplace, a workflow may be initiated where theuser's requisition takes time to get approval. Procurement rules,workflow chains, holidays and general busy-ness can affect theefficiencies involved in this and can cause requisitions to get stuckbeyond reasonable timeframes. The Auto Requisition Reminder CognitiveAdvisor learns about approval patterns and times based on user, productcategory, cart size, historical approval chain times, urgency and such,and automatically manages pushing reminders and escalations to work onbehalf of the user to get their requisition through to purchasing.

Another example is a “Requisition Rejection Prediction” CognitiveAdvisor. When Procurement rules are complex and purchase policies arecomplex, confusing or simply not known to the buying user, requisitionscan be made by users that may get rejected, wasting time and effort onpart of all involved, and further creating a bad experience for users.The Requisition Rejection Prediction Cognitive Advisor learns fromrejected requisitions, the conditions and contexts in which requisitionsmay be rejected, and provides warning with backing details to the userproactively even before a requisition is submitted, thereby arming theuser with insights that could save a costly and painful process fromeven beginning up front.

Cognitive Advisors may also be implemented for Enhancing a ProcurementBuyer's Experience. Procurement Buyers are specialists in one or moreproduct categories and are usually responsible for sourcing efforts andcreating supplier contracts with the promise of great savings, andfurther deploying such sourced contracts and suppliers into theProcurement Marketplace and guiding users to buy from said contracts toactualize the potential savings promised through those contracts.

Often what occurs is that users' shopping patterns don't match with thestrategy and contracts set up by Procurement Buyers in differentcategories; i.e. the user shopping behavior can be said to benon-compliant, and this does not help to tap into procurement rules andexpectations, and in turn, the savings are not actualized. A fewspecific situations would need to be addressed in this regard. Forexample, procurement buyers would have to provide actual contractinformation to the system so that the information contained in it can beused in real-time by the system. Systems implemented based on thisdisclosure may provide the means for the procurement buyers to do so. Asanother example, Suppliers would need to be carefully organized intocategories, and tagged appropriately for their specific attributes (e.g.minority supplier, woman-owned supplier, veteran supplier etc.), so thatthe guided buying capability of an e-procurement system can operate asexpected, suitably rank ordering product selections in the universalsearch experience. A system implemented based on this disclosure mayprovide the tools for this to be setup correctly.

On the other end of the spectrum, procurement buyers believe that theirsuppliers are providing them with the best prices, when the groundreality as witnessed in real-time may not be like this at all. Severalspecific situations emerge that it may be beneficial to solve. Forexample, suppliers promise a price in the contract, but the pricesprovided in the product listings in real-time are not matching what isprovided in the contract. Real-time price audit checks, such as could beincorporated into a system implemented based on this disclosure, mayprovide a way to address this potential discrepancy that wouldcontribute to savings leaks. As another example, the contracted pricefrom suppliers may not be the best, or may not even be somewhatcompetitive, when compared with what other suppliers may provide inreal-time for the same products being considered by users. In somesystems implemented based on this disclosure, real-time price dispersionanalytics may be provided as a way to address this potential discrepancyby providing the users with real-time alternative supplier and productchoices, and by providing procurement buyers with real-time analytics onthe same

These capabilities cited enable the Procurement buyers to get suitableanalytics to renegotiate with suppliers or choose to continue tobusiness with them or drop them from their sourcing mix. Beyond these,Cognitive Advisors can bring another generational leap in enhancing theprocurement buyer's experience. For example, one implemented ContentAdvisor may be a “Savings Predictor” Cognitive Advisor. For thecategories under management by the procurement buyer, the SavingsPredictor Cognitive Advisor analyzes savings actualized through theProcurement Marketplace to-date and learning from current buying andsavings patterns within the category, creates a prediction about thelikelihood of hitting the savings target set for the year in thatcategory. Such savings prediction may be included in the categorysection of the procurement buyer's personal dashboard. This would enablethe procurement buyer to examine the savings strategies and currentimplementation, and make suitable modification to strategies,configuration and other aspects to ensure that predicted savings can infact be actualized.

Another example may include a “Supplier Savings Leak” Cognitive Advisor.By analyzing the spending patterns within a given supplier, inreal-time, in the categories that a procurement buyer is responsiblefor, and learning from the transactional level as well as from thestrategic level, the savings lost to date, due to the supplier's pricingnot being competitive, this Cognitive Advisor prepares a prediction ofthe scale of the savings leak occurring with this supplier continuing toremain in the buying mix through the remainder period of the year. Thiswould enable the procurement buyer to proactively modify the suppliermix or do mid-stream renegotiation with the suppliers in question toremedy the savings leaks.

Another example may include a “User Savings Leak” Cognitive Advisor. Byanalyzing the spending patterns across users, in real-time, in thecategories that a procurement buyer is responsible for, and learningfrom the transactional details and contexts, the savings lost to date,due to the users' buying patterns and supplier choices, this CognitiveAdvisor prepares a prediction of the scale of the savings leak occurringwith each user and across users, and their root causes, and presentsthat to the procurement buyer. This would enable the procurement buyerto take suitable troubleshooting and specific steps to remedy the buyingpatterns of the users-at-risk who are contributing to the highest levelsof savings leaks.

Another example may include a “Strategy Recommender” Cognitive Advisor.Using a mix of relevant savings strategies, and real-time learning ofactual user spend behavior, savings leaks, supplier pricecompetitiveness, supplier satisfaction and more, the StrategyRecommender Cognitive Advisor proposes strategies and configurationsthat are more likely to hit strategic objectives around spend andsavings.

It should be understood that any one or more of the teachings,expressions, embodiments, examples, etc. described herein may becombined with any one or more of the other teachings, expressions,embodiments, examples, etc. that are described herein. Thefollowing-described teachings, expressions, embodiments, examples, etc.should therefore not be viewed in isolation relative to each other.Various suitable ways in which the teachings herein may be combined willbe readily apparent to those of ordinary skill in the art in view of theteachings herein. Such modifications and variations are intended to beincluded within the scope of the claims.

Having shown and described various embodiments of the present invention,further adaptations of the methods and systems described herein may beaccomplished by appropriate modifications by one of ordinary skill inthe art without departing from the scope of the present invention.Several of such potential modifications have been mentioned, and otherswill be apparent to those skilled in the art. For instance, theexamples, embodiments, geometrics, materials, dimensions, ratios, steps,and the like discussed above are illustrative and are not required.Accordingly, the scope of the present invention should be considered interms of the following claims and is understood not to be limited to thedetails of structure and operation shown and described in thespecification and drawings.

3.5 Real-Time Adaptive Relevance for Procurement [0212]

Federated e-procurement systems provide both significant complexity indelivering relevant results as well as unique ways to address theproblem of relevance within real-time universal search results fromdistributed disparate sources. Search engine behavior in a Federatede-procurement system can be compared and contrasted with the behaviorsin traditional search systems, traditional federated search systems, andtraditional e-procurement systems. An illustration of one way ofperforming this comparison that may apply to some embodiments isprovided in FIG. 33.

For example, traditional search systems 3302 typically do not know a lotabout the user or the organization context in detail such as userrelationships to departments and purchasing permissions etc. but do haveaccess to all the data a priori (i.e. they can search across only thosedata sources from which the data has been gathered and indexed forsearching in advance). Many traditional federated search systems 3304have neither the deep knowledge about users, nor have all the data apriori (i.e. they only find the data needed during federated searchingof multiple distributed searchable sources). Many traditionale-procurement systems 3306 which do know a lot about the user AND haveall the data a priori suffer from having to treat remote (e.g.,punchout) sources as separate islands that the user cannot search acrossand would have to visit one at a time to conduct their searches forpurchasing. In contrast, federated e-procurement systems 3308 may know alot about the user, but little about all the data, often discovering thedata needed in real-time during federated universal search contexts.

There are a variety of challenges in establishing relevance ranking inone or more of these systems. For example, traditional search engines3302 typically have access to all the content that they make availablefor searching, and in turn, can use traditional relevance rankingfactors including term frequency, inverse document frequency, documentcitation score and so forth.

Traditional federated search engines 3304 often cannot apply theapproaches taken by traditional search engines to computing relevancescores for documents. In turn, federated search engines often usedifferent approaches to relevance scoring, depending on the context inwhich they are deployed. An airline shopping engine, which might searchacross multiple airline websites, databases and booking sites, mayestablish relevance based on the price of the itineraries in the resultsor use other factors to create a proprietary relevance method. On theother hand, a federated e-procurement search engine 3308 might establishrelevance based on recency of the published article, or the citations orviews of the published articles from each connected journal source.

Many traditional e-procurement search engines 3306 rely on all thesupplier and product data to be loaded in advance into their systems,typically in the form of so-called “hosted catalogs”. With all the dataavailable in advance, a search engine may be constructed in the normalways that traditional search engines are constructed. Relevance rankingmay borrow from techniques used by traditional search engines but mayfurther be tuned based on richness of product information, supplierrankings, product rankings, product popularity, price and other factors.

Implementing a real-time universal search in federated e-procurementsystems 3308 searching across hosted catalogs, punchout supplierwebsites, and other content sources, naturally face the challenge offinding and handling search results from disparate sources in real-time.For example, they do not necessarily have a priori knowledge of thecontent within remote, disparate sources, and would have to incorporateunique relevance ranking approaches to organize the search results.

Federated e-procurement systems 3308, such as those disclosed herein,may advantageously address real-time relevance ranking using differentapproaches, since none of the approaches taken by the other three typesof systems being compared here would be able to address what is neededin a real-time federated universal search context encountered in aFederated e-procurement system.

Systems such as those disclosed herein may provide the asynchronousstreaming of items, a core stream-based relevancy technique, the use ofvarious relevance factors such as supplier preference, supplier itemrelevance, category relevance, and user preference. A configurablerelevance tuner may also be provided that gives the organization, totalcontrol over the relevance factors for their specific environment.

While real-time cross-catalog relevance tuning is an advantage fore-procurement systems that implement a real-time federated universalsearch across distributed disparate sources, there are other issues thatremain unaddressed. For example, remote sources could behave verydifferently in their handling of a given query. Differences in querygrammar, native relevance ranking, and more can result in an overalldifferent treatment across punchout supplier sources and other remotesources. As a result, in real-time universal federated search results inProcurement, it is possible to get some irrelevant results, even whenrelying on inputs such as “preferred” sources. The presence ofirrelevant search results causes dissatisfaction on part of the usersand could erode confidence in the real-time universal search paradigmaltogether.

Despite efforts to improve in relevance ranking and tuning, the risk ofirrelevant results coming into the mix is real in Federatede-procurement systems. However, Federated e-procurement systems do havericher knowledge of users, such as the department and divisions theywork in, their peers who are user of the system, past product selectionsand purchases by themselves and other users, and other information.

The disclosed technology may, in some embodiments, enable Federatede-procurement systems to vastly reduce or eliminate irrelevant resultsthat emerge from real-time universal federated searching of disparatesources in e-procurement. Rather than relying on relevance scoring andranking approaches alone, some embodiments may be adapted to identifyand eliminate irrelevant results from a real-time federated universalsearch result set in a Federated e-procurement context.

Instead of relying solely on relevance scoring, relevance ranking, andtuning as the only approach to ensuring relevant results in a Federatede-procurement system, there is the opportunity to implement an adaptiverelevance filtering approach that eliminates irrelevant results frombeing shown to the user in a Federated e-procurement system. An aspectof this adaptive relevance filtering approach is a Relevance LearningEngine, shown in FIG. 34. The purpose of this Relevance Learning Engineis that it continuously learns about the likely relevance of any givenproduct result item to a given query for a given category for a givenuser context. As an example, the Relevance Learning Engine may beconfigured to continually learn from the following: direct signals ofirrelevant results, indirect signals of preferred and not-preferredresults, and real-time adaptive filtering of results.

As seen in FIG. 35, when the Relevance Learning Engine is applied in thefederated real-time universal search process, every relevance scoredsearch result item may be sent to the Relevance Learning Engine 3402which provides an indicator 3502 whether the item is Irrelevant to thecontext provided, and a degree of confidence 3504 in that assessment. Insome embodiments, a federated search results handler may then apply thisirrelevance indicator where there is high confidence and remove themfrom the user's view.

As shown in FIG. 36, in some embodiments a Relevance Learning Modelacross different organizations 3602 may also be further provided, withsuitable anonymization as needed, to a Global Product Item MasterDatabase to continually grow the wealth of knowledge about any productencountered in e-procurement across the entire set of organizationsusing it. The growing richness of information around product, productselection and preference, product purchase, product relevance indifferent contexts in the Global Product Item Master Database may beused to provide continual enrichment of the Universal Search experienceacross any given organization, and the functioning of Cognitive Advisorswithin any given organization. In some embodiments, Cognitive Advisorsmay include processes that process such information in real-time toprovide users various features and recommendations based upon theirconfigured purpose and input data.

Whether implemented individually or in combination, a Relevance LearningEngine and a Relevance Ranking may each play distinct roles in theFederated Universal Search in e-procurement. FIG. 37 shows an example ofa process for adaptive relevance filtering in e-procurement. TheRelevance Learning Engine approach outlined herein aims to deliverrelevant results by focusing on finding and eliminating irrelevantresults. As shown in FIG. 37, one feature that may be provided in someembodiments for a real-time relevance processing pipeline is that it issuitable in a real-time federated universal search for products inFederated e-procurement.

With each search result 3704 in the universal search result set gatheredin real-time, the user is presented with an action link (or button)called “Not Relevant”. A user can mark a given search result as “NotRelevant” as they see fit. When the user marks a given search result as“Not Relevant”, the system may perform one or more of the following:record the entire context of the user, the query context, the resultcontext including the source which produced the result and its rankorder and more, hide this result item from the search results for thisuser, push this action with complete context to the Relevance LearningEngine, which may be configured to learn about irrelevant results forgiven contexts, and provide access to hidden results via a separate linkprovided at the top of the search results (e.g., “Show Hidden Results(n)”, where “n” is the number of hidden results).

The Relevance Learning Engine may advantageously improve the relevanceof search results in a variety of ways. For example, when the same userdoes the same query later, the previously hidden result may be hiddenfrom the user's view, which then shows only relevant results 3704. Thiscan be accomplished by, for example, associating values withcharacteristics of the results (e.g., IrrelevantTag=1,IrrelevantTagConfidence=100) that are available to the RelevanceLearning Engine. When the same user does a similar query at any time,the previously hidden result is hidden or suitably ranked lower based onthe similarity of the query to the original query context in which theresult item was originally hidden. This leads to ranked relevant results3706. This can be accomplished by, for example, examining a value (e.g.,IrrelevantTag) returned by the Relevance Learning Engine for theparticular result context.

When other users in the same department/division as the given user,perform the same or similar queries, the previously hidden results mayhave their relevance score dampened significantly to push them down thesearch result list based upon the associated value (e.g., IrrelevantTagand IrrelevantTagConfidence).

When other users in different departments or divisions from a particularuser perform the same or similar queries, the previously hidden resultsmay have their relevance score dampened somewhat to push them towardsthe later part of the search result list, again based upon theassociated values and characteristics (e.g., IrrelevantTag andIrrelevantTagConfidence).

Other users are given the option, from within the interface to seeHidden search results, to Unhide a result that they deem relevant, butwas hidden before. This signal is also captured with complete contextand sent into the relevance learning engine. Over time, the relevancelearning engine adapts and improves the relevance of search results byhiding or lowering the rank of potentially irrelevant search results.

FIG. 38 shows an example of a dataset produced by the act of one or moreusers hiding a given search result item for a given query. The action3802 associated with each user 3804 may be performed to influence searchresults containing the same item for other users in different user andquery contexts.

There are a number of advantages that this approach of identifyingirrelevant results from direct signals may provide. For example, someembodiments may take advantage of the fact that a Federatede-procurement provider may know the user identity, and practically allsearch engine implementations tend to be created with a focus on lettingthe users view and consume a search result set produced by the searchengine. As a result, users cannot manipulate the result set directly. Bycontrast, some embodiments, may offer the users the direct ability tomodify their personal search experience by marking select products inthe search result listing as “Not Relevant.”

As another advantage, since Federated e-procurement systems are usuallytailored to specific organization needs, and the users of such tailoredsystem are only the authorized users of the organization, the risk thatexists in traditional systems of users “gaming” the search engine fortheir own motives does not exist. As a result, this is a trusted usercontext in which it is safe to let the user modify their universalsearch result sets as they deem personally useful.

As another example advantage, the direct signal of “Not Relevant” from agiven user in a given context for a given search for a given productitem listed in universal search results, not only directly benefits themby removing the clutter of irrelevant results from their result set, butmay also enable the entire organization to benefit from it through theuse of the Relevance Learning Engine. Other advantages and benefitsexist, and will be apparent to those of ordinary skill in the art inlight of this disclosure.

Additionally, in some embodiments, irrelevant results, preferredresults, and not-preferred results may also be identified based uponindirect signals. In a Federated e-procurement system, there are manyindirect signals of a given user's preference or non-preference for agiven product in a given context. Here are some specific types of“indirect signals” of product preference. Some examples are describedbelow.

For a given query, in the universal search result set, when a user goespast, say, the first 5 results and first clicks on the 6th search resultto see their details, or perhaps adds the 8th search result directly tothe cart while ignoring the first 7 results, these are strong signals ofuser's preference for one search result over another.

Clicking on an item detail page, adding an item to be compared withother items, saving the item as a favorite, adding an item to a bundleor order guide, adding an item to the cart, purchasing a given item . .. all these are increasingly stronger signals of preference for a givensearch result item over another (a given product over another). Itemsthat are overlooked and not even clicked on to view their details areclearly not preferred by the user.

Typically, these signals are not utilized in a relevance learningprocess. However, some embodiments of the disclosed technology mayimplement the entire query and search results interaction as well as theinteraction with all aspects that deal with individual items (likecompare, favorites, bundles, order guides, cart etc.), and thus capturethese indirect signals on a granular level about user preferences inevery given query context. This is fed into the Relevance LearningEngine. The Relevance Learning Engine in turn formulates a model ofpreference for an item in a context across all users.

Effectively, the signals of preference are factored into the RelevanceLearning Engine's dataset as values such as the “IrrelevantTag” scorefor any given product item in any given query and user context. Thisapproach of learning from indirect signals of product preferencesprovides a variety of advantages. For example, in some embodiments ane-procurement platform may take advantage of the fact that it knows theuser identity, and knows a lot more about the user's context.

Similarly, some embodiments may extrapolate the value of these indirectsignals from this user to their value to other peers of that user intheir department or division, and further out to other users in theirbusiness region, and still further out to users in the organization as awhole in a suitable manner through the Relevance Learning Engine.

By default, a real-time universal federated search system, would try todo real-time streaming of results into the end-user interface toengender a snappy, high performance, speedy user experience. Forcertain, more complex queries, despite the many different ways toenhance relevance ranking, the search results may still be filled withirrelevant results, due mostly in part to the poor handling of complexqueries in punchout supplier sites or other remote sources.

To handle this type of need, a system implemented based on thisdisclosure may implement a “Give me only relevant results” option, aso-called “High relevance only mode”. When the user chooses to see onlyrelevant results, the system may perform one or more of the following:as raw search results are streaming from multiple sources, the systemqueues each raw search result for fetching their full result pages, thequeue is adaptively managed so that multiple requests for pages from anygiven remote source is politely handled, while ensuring that the userexperience is not slowed down, each full result page is processed toextract rich metadata including description, attributes and values fromproduct specifications and more, each item detail record thus gatheredfrom each full result page is queued for adding into an in-memoryfull-text index, a new relevance score is assigned to all pages, whichcan be thought of as a “Deep Relevance Score” from real-time analysis ofevery search result page for relevance based on content about theproduct retrieved from its source in the moment (e.g., FIG. 39 shows anexample process for Deep Relevance Scoring with real-time adaptivefiltering 3902), the full query context (including all the complex queryterms and constraints the user may have provided) is applied on thein-memory full-text index 3904, and any matching result is scored andtagged with other standard criteria for relevance ranking and streamedin real-time to be presented to the end user, the complete details ofboth relevant and irrelevant items are fed into the Relevance LearningEngine 3906, and the item detail record is dispatched into the GlobalProduct Item Master database 3908 for updating information on knownitems. This real-time adaptive filtering approach streams to the userinterface only relevant results in real-time, thus compensating for thevagaries and limitations of remote sources and punchout supplier sites.

It should be understood that any one or more of the teachings,expressions, embodiments, examples, etc. described herein may becombined with any one or more of the other teachings, expressions,embodiments, examples, etc. that are described herein. Thefollowing-described teachings, expressions, embodiments, examples, etc.should therefore not be viewed in isolation relative to each other.Various suitable ways in which the teachings herein may be combined willbe readily apparent to those of ordinary skill in the art in view of theteachings herein. Such modifications and variations are intended to beincluded within the scope of the claims.

Having shown and described various embodiments of the present invention,further adaptations of the methods and systems described herein may beaccomplished by appropriate modifications by one of ordinary skill inthe art without departing from the scope of the present invention.Several of such potential modifications have been mentioned, and otherswill be apparent to those skilled in the art. For instance, theexamples, embodiments, geometrics, materials, dimensions, ratios, steps,and the like discussed above are illustrative and are not required.Accordingly, the scope of the present invention should be considered interms of the following claims and is understood not to be limited to thedetails of structure and operation shown and described in thespecification and drawings.

4. Example Processes

FIG. 40 discussed below is shown in simplified, schematic format forpurposes of illustrating a clear example and other embodiments mayinclude more, fewer, or different elements connected in various manners.FIG. 40 is intended to disclose an algorithm, plan or outline that canbe used to implement one or more computer programs or other softwareelements which when executed cause performing the functionalimprovements and technical advances that are described herein.Furthermore, the flow diagrams herein are described at the same level ofdetail that persons of ordinary skill in the art ordinarily use tocommunicate with one another about algorithms, plans, or specificationsforming a basis of software programs that they plan to code or implementusing their accumulated skill and knowledge.

FIG. 40 illustrates an example process performed by a server of enablingmultiple cognitive advisors in a cognitive advisor framework forprocessing user queries.

In some embodiments, in step 4002, the server is programmed orconfigured to maintain a database containing buyer data and supplierdata. The buyer data is related to buyer organization hierarchies andbuyer online activities, the buyer online activities including browsingproduct pages, selecting product data for review, adding products toshopping lists or carts, providing product or supplier reviews,obtaining requisitions or purchase orders, and placing product orders.The supplier data is related to suppliers, products, and invoices. Thebuyer organizational hierarchies can include data related to a company,a division, a region, or a department.

In some embodiments, in step 4004, the server is programmed orconfigured to receive a query associated with a buyer account.

In some embodiments, in step 4006, the server is programmed orconfigured to compute a query context for the query that includes buyeritems and additional items derived from the query based on the database.The buyer items include a level of an organizational hierarchy in whichthe buyer account fits. The buyer items can also include a geographiclocation or a supplier preference of the buyer account.

In some embodiments, the query identifies a specific product. Computingthe query context comprises including as the additional items a productcategory of the specific product or a list of products similar to thespecific products based on a similarity measure.

In some embodiments, in step 4008, the server is programmed orconfigured to select a plurality of cognitive advisors from a group ofcognitive advisors based on the query context. Each of the group ofcognitive advisors being a digital model has a distinct set of inputparameters and output data. The output data is a recommendation of oneor more products or supplier accounts.

In some embodiments, a specific cognitive advisor of the group ofcognitive advisors has a level of an organizational hierarchy as aninput parameter and is configured to search the buyer data associatedwith buyer accounts that fit in the level.

In some embodiments, the server can be configured to further determinean execution order among the plurality of cognitive advisors. In step4010, the server is programmed or configured to execute the plurality ofcognitive advisors to generate a corresponding plurality of lists ofresults in the execution order.

In some embodiments, in step 4012, the server is programmed orconfigured to cause displaying a summary of the plurality of lists ofresults and detail related to a specific list of results of theplurality of lists of results. The server can be configured to furthercause displaying an option to view an explanation for the specific listof results on one page. In some embodiments, the server can beconfigured to further determine a display order among the plurality ofcognitive advisors such that the summary of the plurality of lists ofresults is in an accordion format, with a plurality of sectionsrespectively for the plurality of lists arranged in the display order.The displaying can comprise, when a particular section of the pluralityof sections is selected, collapsing any other section that is expanded,expanding the particular section, and replacing detail related to thespecific list of results by detail related to a particular list ofresults corresponding to the particular section.

In some embodiments, the server is programmed or configured to record aselection of the particular section or a selection of an entry on theparticular list of results in the database.

5. Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by at least one computing device. The techniques may beimplemented in whole or in part using a combination of at least oneserver computer and/or other computing devices that are coupled using anetwork, such as a packet data network. The computing devices may behard-wired to perform the techniques, or may include digital electronicdevices such as at least one application-specific integrated circuit(ASIC) or field programmable gate array (FPGA) that is persistentlyprogrammed to perform the techniques, or may include at least onegeneral purpose hardware processor programmed to perform the techniquespursuant to program instructions in firmware, memory, other storage, ora combination. Such computing devices may also combine custom hard-wiredlogic, ASICs, or FPGAs with custom programming to accomplish thedescribed techniques. The computing devices may be server computers,workstations, personal computers, portable computer systems, handhelddevices, mobile computing devices, wearable devices, body mounted orimplantable devices, smartphones, smart appliances, internetworkingdevices, autonomous or semi-autonomous devices such as robots orunmanned ground or aerial vehicles, any other electronic device thatincorporates hard-wired and/or program logic to implement the describedtechniques, one or more virtual computing machines or instances in adata center, and/or a network of server computers and/or personalcomputers.

FIG. 41 is a block diagram that illustrates an example computer systemwith which an embodiment may be implemented. In the example of FIG. 41,a computer system 4100 and instructions for implementing the disclosedtechnologies in hardware, software, or a combination of hardware andsoftware, are represented schematically, for example as boxes andcircles, at the same level of detail that is commonly used by persons ofordinary skill in the art to which this disclosure pertains forcommunicating about computer architecture and computer systemsimplementations.

Computer system 4100 includes an input/output (I/O) subsystem 4102 whichmay include a bus and/or other communication mechanism(s) forcommunicating information and/or instructions between the components ofthe computer system 4100 over electronic signal paths. The I/O subsystem4102 may include an I/O controller, a memory controller and at least oneI/O port. The electronic signal paths are represented schematically inthe drawings, for example as lines, unidirectional arrows, orbidirectional arrows.

At least one hardware processor 4104 is coupled to I/O subsystem 4102for processing information and instructions. Hardware processor 4104 mayinclude, for example, a general-purpose microprocessor ormicrocontroller and/or a special-purpose microprocessor such as anembedded system or a graphics processing unit (GPU) or a digital signalprocessor or ARM processor. Processor 4104 may comprise an integratedarithmetic logic unit (ALU) or may be coupled to a separate ALU.

Computer system 4100 includes one or more units of memory 4106, such asa main memory, which is coupled to I/O subsystem 4102 for electronicallydigitally storing data and instructions to be executed by processor4104. Memory 4106 may include volatile memory such as various forms ofrandom-access memory (RAM) or other dynamic storage device. Memory 4106also may be used for storing temporary variables or other intermediateinformation during execution of instructions to be executed by processor4104. Such instructions, when stored in non-transitory computer-readablestorage media accessible to processor 4104, can render computer system4100 into a special-purpose machine that is customized to perform theoperations specified in the instructions.

Computer system 4100 further includes non-volatile memory such as readonly memory (ROM) 4108 or other static storage device coupled to I/Osubsystem 4102 for storing information and instructions for processor4104. The ROM 4108 may include various forms of programmable ROM (PROM)such as erasable PROM (EPROM) or electrically erasable PROM (EEPROM). Aunit of persistent storage 4110 may include various forms ofnon-volatile RAM (NVRAM), such as FLASH memory, or solid-state storage,magnetic disk or optical disk such as CD-ROM or DVD-ROM, and may becoupled to I/O subsystem 4102 for storing information and instructions.Storage 4110 is an example of a non-transitory computer-readable mediumthat may be used to store instructions and data which when executed bythe processor 4104 cause performing computer-implemented methods toexecute the techniques herein.

The instructions in memory 4106, ROM 4108 or storage 4110 may compriseone or more sets of instructions that are organized as modules, methods,objects, functions, routines, or calls. The instructions may beorganized as one or more computer programs, operating system services,or application programs including mobile apps. The instructions maycomprise an operating system and/or system software; one or morelibraries to support multimedia, programming or other functions; dataprotocol instructions or stacks to implement TCP/IP, HTTP or othercommunication protocols; file processing instructions to interpret andrender files coded using HTML, XML, JPEG, MPEG or PNG; user interfaceinstructions to render or interpret commands for a graphical userinterface (GUI), command-line interface or text user interface;application software such as an office suite, internet accessapplications, design and manufacturing applications, graphicsapplications, audio applications, software engineering applications,educational applications, games or miscellaneous applications. Theinstructions may implement a web server, web application server or webclient. The instructions may be organized as a presentation layer,application layer and data storage layer such as a relational databasesystem using structured query language (SQL) or no SQL, an object store,a graph database, a flat file system or other data storage.

Computer system 4100 may be coupled via I/O subsystem 4102 to at leastone output device 4112. In one embodiment, output device 4112 is adigital computer display. Examples of a display that may be used invarious embodiments include a touch screen display or a light-emittingdiode (LED) display or a liquid crystal display (LCD) or an e-paperdisplay. Computer system 4100 may include other type(s) of outputdevices 4112, alternatively or in addition to a display device. Examplesof other output devices 4112 include printers, ticket printers,plotters, projectors, sound cards or video cards, speakers, buzzers orpiezoelectric devices or other audible devices, lamps or LED or LCDindicators, haptic devices, actuators or servos.

At least one input device 4114 is coupled to I/O subsystem 4102 forcommunicating signals, data, command selections or gestures to processor4104. Examples of input devices 4114 include touch screens, microphones,still and video digital cameras, alphanumeric and other keys, keypads,keyboards, graphics tablets, image scanners, joysticks, clocks,switches, buttons, dials, slides, and/or various types of sensors suchas force sensors, motion sensors, heat sensors, accelerometers,gyroscopes, and inertial measurement unit (IMU) sensors and/or varioustypes of transceivers such as wireless, such as cellular or Wi-Fi, radiofrequency (RF) or infrared (IR) transceivers and Global PositioningSystem (GPS) transceivers.

Another type of input device is a control device 4116, which may performcursor control or other automated control functions such as navigationin a graphical interface on a display screen, alternatively or inaddition to input functions. Control device 4116 may be a touchpad, amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 4104 and for controllingcursor movement on display 4112. The input device may have at least twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane.Another type of input device is a wired, wireless, or optical controldevice such as a joystick, wand, console, steering wheel, pedal,gearshift mechanism or other type of control device. An input device4114 may include a combination of multiple different input devices, suchas a video camera and a depth sensor.

In another embodiment, computer system 4100 may comprise an internet ofthings (IoT) device in which one or more of the output device 4112,input device 4114, and control device 4116 are omitted. Or, in such anembodiment, the input device 4114 may comprise one or more cameras,motion detectors, thermometers, microphones, seismic detectors, othersensors or detectors, measurement devices or encoders and the outputdevice 4112 may comprise a special-purpose display such as a single-lineLED or LCD display, one or more indicators, a display panel, a meter, avalve, a solenoid, an actuator or a servo.

When computer system 4100 is a mobile computing device, input device4114 may comprise a global positioning system (GPS) receiver coupled toa GPS module that is capable of triangulating to a plurality of GPSsatellites, determining and generating geo-location or position datasuch as latitude-longitude values for a geophysical location of thecomputer system 4100. Output device 4112 may include hardware, software,firmware and interfaces for generating position reporting packets,notifications, pulse or heartbeat signals, or other recurring datatransmissions that specify a position of the computer system 4100, aloneor in combination with other application-specific data, directed towardhost 4124 or server 4130.

Computer system 4100 may implement the techniques described herein usingcustomized hard-wired logic, at least one ASIC or FPGA, firmware and/orprogram instructions or logic which when loaded and used or executed incombination with the computer system causes or programs the computersystem to operate as a special-purpose machine. According to oneembodiment, the techniques herein are performed by computer system 4100in response to processor 4104 executing at least one sequence of atleast one instruction contained in main memory 4106. Such instructionsmay be read into main memory 4106 from another storage medium, such asstorage 4110. Execution of the sequences of instructions contained inmain memory 4106 causes processor 4104 to perform the process stepsdescribed herein. In alternative embodiments, hard-wired circuitry maybe used in place of or in combination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperation in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage 4110. Volatilemedia includes dynamic memory, such as memory 4106. Common forms ofstorage media include, for example, a hard disk, solid state drive,flash drive, magnetic data storage medium, any optical or physical datastorage medium, memory chip, or the like.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise a bus of I/O subsystem 4102. Transmission media canalso take the form of acoustic or light waves, such as those generatedduring radio-wave and infra-red data communications.

Various forms of media may be involved in carrying at least one sequenceof at least one instruction to processor 4104 for execution. Forexample, the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over acommunication link such as a fiber optic or coaxial cable or telephoneline using a modem. A modem or router local to computer system 4100 canreceive the data on the communication link and convert the data to beread by computer system 4100. For instance, a receiver such as a radiofrequency antenna or an infrared detector can receive the data carriedin a wireless or optical signal and appropriate circuitry can providethe data to I/O subsystem 4102 such as place the data on a bus. I/Osubsystem 4102 carries the data to memory 4106, from which processor4104 retrieves and executes the instructions. The instructions receivedby memory 4106 may optionally be stored on storage 4110 either before orafter execution by processor 4104.

Computer system 4100 also includes a communication interface 4118coupled to bus 4102. Communication interface 4118 provides a two-waydata communication coupling to network link(s) 4120 that are directly orindirectly connected to at least one communication networks, such as anetwork 4122 or a public or private cloud on the Internet. For example,communication interface 4118 may be an Ethernet networking interface,integrated-services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of communications line, for example an Ethernet cableor a metal cable of any kind or a fiber-optic line or a telephone line.Network 4122 broadly represents a local area network (LAN), wide-areanetwork (WAN), campus network, internetwork or any combination thereof.Communication interface 4118 may comprise a LAN card to provide a datacommunication connection to a compatible LAN, or a cellularradiotelephone interface that is wired to send or receive cellular dataaccording to cellular radiotelephone wireless networking standards, or asatellite radio interface that is wired to send or receive digital dataaccording to satellite wireless networking standards. In any suchimplementation, communication interface 4118 sends and receiveselectrical, electromagnetic or optical signals over signal paths thatcarry digital data streams representing various types of information.

Network link 4120 typically provides electrical, electromagnetic, oroptical data communication directly or through at least one network toother data devices, using, for example, satellite, cellular, Wi-Fi, orBLUETOOTH technology. For example, network link 4120 may provide aconnection through a network 4122 to a host computer 4124.

Furthermore, network link 4120 may provide a connection through network4122 or to other computing devices via internetworking devices and/orcomputers that are operated by an Internet Service Provider (ISP) 4126.ISP 4126 provides data communication services through a world-widepacket data communication network represented as internet 4128. A servercomputer 4130 may be coupled to internet 4128. Server 4130 broadlyrepresents any computer, data center, virtual machine or virtualcomputing instance with or without a hypervisor, or computer executing acontainerized program system such as DOCKER or KUBERNETES.

Server 4130 may represent an electronic digital service that isimplemented using more than one computer or instance and that isaccessed and used by transmitting web services requests, uniformresource locator (URL) strings with parameters in HTTP payloads, APIcalls, app services calls, or other service calls. Computer system 4100and server 4130 may form elements of a distributed computing system thatincludes other computers, a processing cluster, server farm or otherorganization of computers that cooperate to perform tasks or executeapplications or services. Server 4130 may comprise one or more sets ofinstructions that are organized as modules, methods, objects, functions,routines, or calls. The instructions may be organized as one or morecomputer programs, operating system services, or application programsincluding mobile apps. The instructions may comprise an operating systemand/or system software; one or more libraries to support multimedia,programming or other functions; data protocol instructions or stacks toimplement TCP/IP, HTTP or other communication protocols; file formatprocessing instructions to interpret or render files coded using HTML,XML, JPEG, MPEG or PNG; user interface instructions to render orinterpret commands for a graphical user interface (GUI), command-lineinterface or text user interface; application software such as an officesuite, internet access applications, design and manufacturingapplications, graphics applications, audio applications, softwareengineering applications, educational applications, games ormiscellaneous applications. Server 4130 may comprise a web applicationserver that hosts a presentation layer, application layer and datastorage layer such as a relational database system using structuredquery language (SQL) or no SQL, an object store, a graph database, aflat file system or other data storage.

Computer system 4100 can send messages and receive data andinstructions, including program code, through the network(s), networklink 4120 and communication interface 4118. In the Internet example, aserver 4130 might transmit a requested code for an application programthrough Internet 4128, ISP 4126, local network 4122 and communicationinterface 4118. The received code may be executed by processor 4104 asit is received, and/or stored in storage 4110, or other non-volatilestorage for later execution.

The execution of instructions as described in this section may implementa process in the form of an instance of a computer program that is beingexecuted, and consisting of program code and its current activity.Depending on the operating system (OS), a process may be made up ofmultiple threads of execution that execute instructions concurrently. Inthis context, a computer program is a passive collection ofinstructions, while a process may be the actual execution of thoseinstructions. Several processes may be associated with the same program;for example, opening up several instances of the same program oftenmeans more than one process is being executed. Multitasking may beimplemented to allow multiple processes to share processor 4104. Whileeach processor 4104 or core of the processor executes a single task at atime, computer system 4100 may be programmed to implement multitaskingto allow each processor to switch between tasks that are being executedwithout having to wait for each task to finish. In an embodiment,switches may be performed when tasks perform input/output operations,when a task indicates that it can be switched, or on hardwareinterrupts. Time-sharing may be implemented to allow fast response forinteractive user applications by rapidly performing context switches toprovide the appearance of concurrent execution of multiple processessimultaneously. In an embodiment, for security and reliability, anoperating system may prevent direct communication between independentprocesses, providing strictly mediated and controlled inter-processcommunication functionality.

6.0. Extensions and Alternatives

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the disclosure,and what is intended by the applicants to be the scope of thedisclosure, is the literal and equivalent scope of the set of claimsthat issue from this application, in the specific form in which suchclaims issue, including any subsequent correction.

What is claimed is:
 1. A computer-implemented method of enablingmultiple cognitive advisors in a cognitive advisor framework forprocessing user queries, comprising: maintaining, by a processor, adatabase containing buyer data and supplier data, the buyer data beingrelated to buyer organization hierarchies and buyer online activities,the buyer online activities including browsing product pages, selectingproduct data for review, adding products to shopping lists or carts,providing product or supplier reviews, obtaining requisitions orpurchase orders, or placing product orders, the supplier data beingrelated to suppliers, products, and invoices; receiving, by theprocessor, a query inputted on a user device associated with a buyeraccount; computing, by the processor, a query context for the query thatincludes buyer items and additional items derived from the query basedon the database, the buyer items being associated with a level of anorganizational hierarchy in which the buyer account fits; selecting aplurality of cognitive advisors from a group of cognitive advisors basedon the query context, each of the group of cognitive advisors being adigital model having a distinct set of input parameters and output data,a specific cognitive advisor of the group of cognitive advisors having alevel of an organizational hierarchy as an input parameter and beingconfigured to search the buyer data associated with buyer accounts thatfit in the inputted level of organization hierarchy, the output databeing a recommendation of one or more products or supplier accounts;executing the plurality of cognitive advisors to generate acorresponding plurality of lists of results; sending, by the processor,instructions to the user device for displaying a summary of theplurality of lists of results and detail related to a specific list ofresults of the plurality of lists of results.
 2. Thecomputer-implemented method of claim 1, further comprising determiningan execution order among the plurality of cognitive advisors theexecuting being performed in the execution order.
 3. Thecomputer-implemented method of claim 1, further comprising determining adisplay order among the plurality of cognitive advisors, the summary ofthe plurality of lists of results being in an accordion format with aplurality of sections respectively for the plurality of lists arrangedin the display order.
 4. The computer-implemented method of claim 3, thedisplaying comprising, when a particular section of the plurality ofsections is selected, collapsing any other section that is expanded,expanding the particular section, and replacing detail related to thespecific list of results by detail related to a particular list ofresults corresponding to the particular section.
 5. Thecomputer-implemented method of claim 4, further comprising recording aselection of the particular section or a selection of an entry on theparticular list of results.
 6. The computer-implemented method of claim1, the query identifying a specific product, the additional itemsderived from the query indicating a product category of the specificproduct or including a list of products similar to the specific productsbased on a similarity measure.
 7. The computer-implemented method ofclaim 1, the buyer items further being associated with a geographiclocation or a supplier preference of the buyer account.
 8. Thecomputer-implemented method of claim 1, further comprising causingdisplaying an option to view an explanation for the specific list ofresults.
 9. The computer-implemented method of claim 1, the specificcognitive advisor being configured to recommend a bundle of productsgiven a specific product identifier or a specific role within the levelof an organizational hierarchy.
 10. One or more non-transitory storagemedia storing instructions which, when executed by one or moreprocessors, cause performance of a method of enabling multiple cognitiveadvisors in a cognitive advisor framework for processing user queries,the method comprising: maintaining, by a processor, a databasecontaining buyer data and supplier data, the buyer data being related tobuyer organization hierarchies and buyer online activities, the buyeronline activities including browsing product pages, selecting productdata for review, adding products to shopping lists or carts, providingproduct or supplier reviews, obtaining requisitions or purchase orders,or placing product orders, the supplier data being related to suppliers,products, and invoices; receiving, by the processor, a query inputted ona user device associated with a buyer account; computing, by theprocessor, a query context for the query that includes buyer items andadditional items derived from the query based on the database, the buyeritems being associated with a level of an organizational hierarchy inwhich the buyer account fits; selecting a plurality of cognitiveadvisors from a group of cognitive advisors based on the query context,each of the group of cognitive advisors being a digital model having adistinct set of input parameters and output data, a specific cognitiveadvisor of the group of cognitive advisors having a level of anorganizational hierarchy as an input parameter and being configured tosearch the buyer data associated with buyer accounts that fit in theinputted level of organization hierarchy, the output data being arecommendation of one or more products or supplier accounts; executingthe plurality of cognitive advisors to generate a correspondingplurality of lists of results; sending, by the processor, instructionsto the user device for displaying a summary of the plurality of lists ofresults and detail related to a specific list of results of theplurality of lists of results.
 11. The one or more non-transitorystorage media of claim 10, the method further comprising determining anexecution order among the plurality of cognitive advisors, the executingbeing performed in the execution order.
 12. The one or morenon-transitory storage media of claim 10, the method further comprisingdetermining a display order among the plurality of cognitive advisors,the summary of the plurality of lists of results being in an accordionformat with a plurality of sections respectively for the plurality oflists arranged in the display order.
 13. The one or more non-transitorystorage media of claim 12, the displaying comprising, when a particularsection of the plurality of sections is selected, collapsing any othersection that is expanded, expanding the particular section, andreplacing detail related to the specific list of results by detailrelated to a particular list of results corresponding to the particularsection.
 14. The one or more non-transitory storage media of claim 13,the method further comprising recording a selection of the particularsection or a selection of an entry on the particular list of results.15. The one or more non-transitory storage media of claim 10, the queryidentifying a specific product, the additional items derived from thequery indicating a product category of the specific product or includinga list of products similar to the specific products based on asimilarity measure.
 16. The one or more non-transitory storage media ofclaim 10, the buyer items further being associated with a geographiclocation or a supplier preference of the buyer account.
 17. The one ormore non-transitory storage media of claim 10, the method furthercomprising causing displaying an option to view an explanation for thespecific list of results.
 18. The one or more non-transitory storagemedia of claim 10, the specific cognitive advisor being configured torecommend a bundle of products given a specific product identifier or aspecific role within the level of an organizational hierarchy.